Guessing the future has never been easy, not even being a scientist.

Guessing the future has never been easy, not even being a scientist.

It is difficult to make predictions, especially about the future

 (Nicholas, 2007)

We very much tend to use and believe in the argumentative fallacy of the truths of science, which is why the following phrases are often used: “science proves that …” “based on a scientific study it is concluded that …”, With the cloak of science, you say and have the absolute truth. Who could question science? Who believes himself above science? However, things are not quite like that.

Science is an activity with which knowledge is generated so, without a doubt, it is of great help for decision-making. Science is based on verifiable facts. Thus, scientists know that some places are flooded because there is evidence of these events on the ground or because there is evidence to suggest that floods could occur. In this example there is knowledge that is applied to assume an event. If greenhouse gases continue to rise, it stands to reason that the climate could change; if a pandemic comes, it is logical that people will suffer from diseases, deaths and the decrease in economic activities. There is no doubt about that, the problem is when will the pandemic occur? When will climate change occur? When will the flood be? When will the collapse be?

Scientists, technicians and data managers have and generate knowledge, but also “they tend to believe they know a little more than they really do”, that is to say, “what they know is not the same as what they think they know” (Nicholas, 2017) and that is usually a problem when it comes to generating alerts about climate change, for example.

Predicting means: announcing in advance (RAE, 2010), in other words, predicting something is “announcing by revelation, science or conjecture something that has to happen”.

Revelation comes from: a) the prefix “re-”, which can be translated as “backwards”; b) the noun “velum”, which is synonymous with “veil”; y c) the suffix “-cion”, which is used to indicate action and effect. So the word “revelation” means “The act of telling something that is secret, making the hidden visible, or anticipating a future event”.

In the same way, conjecture is defined as the “ judgment that is formed as a result of making observations or analyzing evidence, in other words ,“ judgment or opinion formed from incomplete or supposed indications or data ”. The term, which comes from the Latin coniectūra .

Well, allow me to quote another phrase “ scientists have no word of honor “, this is said like this because what is true today, another scientist will explain it better in the future, with more knowledge, with better tools With better data, better knowledge can be generated, without a doubt, or as one referee said in one of my articles: “ With more work it would be better “.

It must be said that humans, including scientists, are very bad at guessing the future. The weather can only be “predicted” for about five to seven days with certainty because we have satellite images that allow us to “see” the clouds, more or less estimate where they are moving, more time for prediction means greater uncertainty .

History allows us to “predict” that year after year during the rainy season there will be hurricanes that affect the east coast of North and Central America, because the trade winds move from east to west. When exactly will the hurricanes be in terms of days? How many will they be in the next year? With what intensity will they arrive? We do not know, there are no precise answers. I mean, we are not that good at predicting the future, we are scientists, not fortune tellers.

In the case of estimating economic growth by country, which is an activity in which a lot of money is spent, biased estimates have been made since the end of the previous year. For January the uncertainty is very, very large, with variations of units, as the year progresses the predictions are refined, there is more data for the prediction and less time for the end of the year but the uncertainty is tenths of the GDP, already for November there is a lot of data, evidence and the certainties are much greater, for December it is no longer a prediction, it is a fait accompli. However, in this example, the appearance of a black swan is latent at any time.

In examples like the previous one, there is always the possibility of the appearance of “black swans” at any time, those unforeseen events that throw away all predictions, forecasts or estimates, as you like to call them. No one predicted that in the year 2020 the world economy would fall dramatically due to the SARS-CoV-2 pandemic, but as always, once the fact is consummated, the “experts” come out to explain that it was clear that it could happen, that it had signs.

In conclusion, “ It is difficult to make predictions, especially about the future” said Nassim Nicholas Taleb (2007) in his very interesting and illustrative book “ The Black Swan ” translated into Castilian as “Black swan, the impact of the highly improbable”.

Your posts and comments from your “colleagues”

Your posts and comments from your “colleagues”

“Publishing is difficult, being recognized is even more”

 (Bautista, 2021)

When I saw my first article published, I got great satisfaction, great joy for accomplishing one of my goals, beginning to harvest the fruits of so much work and effort. I soon understood that with publications, recognition does not always come to work, at least not from all colleagues and friends. I comment on this, my dear future doctors of science so that you are aware of what awaits you, some comments to your publications will be:

  • Congratulations, but it is in Spanish and the language of science is English
  • How nice, it’s a shame that it was published in a national magazine and only recognized by CONACYT.
  • It’s good that you already publish in English, but the magazine is not JCR (Journal Citation Reports) and has no impact factor

  • What a shame that the magazine in which you publish has a low impact factor
  • That magazine, although it is JCR and has a high impact factor, I think, as it feels, that it is from a predatory publisher
  • Be careful where you publish because not all JCR and impact factor magazines are good
  • This topic is not important for the institution
  • Ok but you are not the first author
  • But you are not an expert on that subject
  • Publishing in high-impact JCR journals, but Open Access is academic colonialism

That is, they will question the topic, magazine, impact factor, editorial, type of publication (open access or by subscription), your place in the list of authors, and everything that is possible, you will not escape criticism. However, the post there is on your CV. Of course you will also receive sincere compliments and congratulations.

Form solid criteria for the selection of the journal, language, review times, costs, prestige, academic profile, think about who your audience will be. This depends on your line of research as well. The best advice I can give you is to verify that the magazine you selected is not on the black list of predatory magazines (predatory journals).

On the other hand, when you work it is only clear that you have to pay the price, in time, of the learning curve, the skills to be a rock star of the academy require a lot of time, dedication and learning with the best tutors. Traveling alone is possible, but it will be difficult, it will be very stimulating, rewarding and magnificent to show yourself that you can alone, but it will be hard.

On the contrary, you will reach the top in less time if you work as a team, where a few write the projects (and there is always money), where there are expert technicians who move in the laboratory like fish in water (and there is always data). There where the great lights are where lines of research, laboratories, undergraduate and graduate degrees are born.

Even better when working in networks, where you have the privilege of knowing other ways of doing science, other laboratories, other equipment, other cultures, so the world becomes small.

Select your path, the shoes, and to walk the path of science.

The new generation of hybrid soil maps, between tradition and modernity

The new generation of hybrid soil maps, between tradition and modernity

Patricia Fragoso, Alberto Pereira, Francisco Bautista  y Gonzalo Zapata

The aim of this work was to develop the digital map of soils for Quintana Roo for a printed scale 1:400000 with 1:50000 work scale data, initially with a geopedological approach and subsequently improved to a digital map.

The map was prepared with data from the formative factors of soils using mathematical methods to infer information in the places where these data are not available. Its elaboration included three stages, the first two following the principles of the geopedological approach consisted in the synthesis of the information generated in the characterization of the geomorphological landscapes (vertical dissection, karst geomorphometrics, failures, geology) and soils, in the third stage incorporating the environmental components (climate and vegetation) and related variables through various statistical analysis (cluster analysis, principal component analysis and classification analysis) the procedure allowed to obtain the pattern of distribution of the Soils to finally develop the model and get to the digital map of soils in the study area.

Vertical dissection, karstic forms (dolines, uvalas and poljes), karst faults densities, and the flooding regime for karstic, bodies of water, and age of parental materials, explain 65% of spatial distribution of soils from Quintana Roo, Mexico.

The analysis of classification denotes that above 83% of soil WRB group assignments are correct.

The soil WRB groups that occupies the territory is the Leptosol, Gleysol and Phaeozem, together occupy 75.6% of surface. Other soil WRB groups are Kastanozem, Regosol, Vertisol, Histosol, Solonchak, Arenosol and Fluvisol.

The map developed with data from the soil forming factors and associated with mathematical methods to infer information in the places where there are no data is an important input for the decision making process.

IVAKY: Index of vulnerability to pollution of yucatecan karstic aquifer

IVAKY: Index of vulnerability to pollution of yucatecan karstic aquifer

The index of the vulnerability of the Yucatecan karstic aquifer (IVAKY) is proposed.

The IVAKY was built based on a geomorphopedological map scale of 1:50 000, which contains the density and type of karst depressions and soil associations in each geomorphopedological unit. The climate factor is included through the length of the rainy period that considers amount, distribution and intensity of the rain.

The three factors (topography, soils and climate) were weighted with the process of hierarchical analysis (AHP) using ArcGis.

It was identified that the ring of sinkholes  and part of the northeast of Yucatan state have the extreme level of vulnerability, where dominated sinkholes in contact with the aquifer and soil as Nudilithic Leptosols, Lithic Leptosols and Rendzic Leptosols, occupying 19% of the state surface.

Low and very low levels of vulnerability are located in southern Yucatan in areas of equal or greater than 50 masl, with low to medium density of karstic depressions (uvala and poljes) and Luvisols, Vertisols and Stagnosols associated with Leptosols ( 12% of the state surface).

In areas with very high levels and high vulnerability, the general population – including producers, entrepreneurs and decision makers – must be informed and made aware that land use is adequately managed, because poor management or activities intensely productive would represent both potential threats and high risks of pollution.

By contrast, in areas with low levels and very low vulnerability, anthropic activities represent a lower risk of aquifer contamination.

Finally, the proposed approach is replicable and could be used to assess the vulnerability of aquifers in regions with similar environmental characteristics in Mexico, Guatemala, Belize, Cuba and the United States of America (Florida).

Aguilar, Y*., F. Bautista, M. Mendoza, O. Frausto, T. Ihl y C. Delgado. 2016. IVAKY: Índice de la vulnerabilidad del acuífero kárstico yucateco a la contaminación. Revisa Mexicana de Ingeniería Química, 15(3): 913-933.

Soil & Environment as a tool for soil environmental functions evaluation

Soil & Environment as a tool for soil environmental functions evaluation

The Soil degradation is a part of total Ecological crisis of due to the Fact That soil is the link of the any Ecosystem. The soil loses its environmental functions (EF ) under the comprehensive loads. One of the key topics of nature protection in the last decade is the evaluation and accounting ecosystem services in human economic activity. Therefore, the search and development of spatial planning tools for areas based on their environmental functions is very important.

The article considers the software for evaluation of environmental functions using TUSEC algorithms (Technique for Soil Evaluation and Categorization) and others index.

The technique implies a score evaluation of basic environmental functions of natural and anthropogenic soils. Environmental functions evaluation allows keeping a balance of benefits and losses at a spatial planning as a result of lower environmental impacts on soil functions.

The central component of the software is a relational DBMS Derby designed in Java using IDE Eclipse. Data on the site, field description and analysis of soil profiles are stored in the database using input tools. Intermediate calculations and evaluation of environmental functions is based on input data by TUSEC models.

The forcasting modeling tool allows calculating the change of EF ranks for different types of land use.

The evaluation results of environmental functions and predictive models can be presented by graphs. Export of tabular and graphical information is possible as well as the spatial reference data into the GIS.

Friendly interface for data input and output and database management is designed for users who do not know SQL query language.


Gallegos, A., Bautista F. and Dubrovin I. 2016. Soil and Environment: Software to evaluate the environmental functions of soils. Software & Systems. (In Russian and English), 114 (2): 195-200. DOI: 10.15827 / 0236-235x.114.195-200.

Bautista F., A. Gallegos* y A, Pacheco*. 2016. Analysis of the environmental functions of soil profile data (Soil & Environment). Skiu. México D.F., México. 72 pp.

Complete english version, see data in:




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