Bibliographic record
Abstract
How much our lives are going to change together with climate changes? Most probably much more than we could expect! This is why it is so important to investigate the consequences of climate changes and the interventions for climate changes mitigation. And this is why it is not a surprise that the number of papers dealing with this topic is increasing more and more, not only in the field of environmental engineering and science, but also in many other fields, including economy, sociology, and even medicine. Rarely an environmental problem has been faced, at the same time, by so many scientists from all over the world. To see at least a small drop of water in a glass which is much more than half empty, we could dare to say that a global crisis is, at least, pushing the science towards solutions which could make our world more sustainable. The recourse to green sources for energy production, and the new paradigm of circular economy, are just two examples of this effect. But research related to the consequences of climate changes is much more varied.The six papers published in this issue face very diverse topics. Non-etheless five of them are somehow related to the mentioned crisis. Strangely enough, these five papers, which allow us a virtual tour from Asia to Europe, start with a research study dealing with tourism!The study presented by Zuo (2025), indeed, is focused on the correlation existing between air pollution and willingness of people to visit a certain area, a question well known to our readers (Amoatey et al., 2022). Using an accurate and reliable mathematical model, the author predicts an intense pollution caused by an excessive concentration of PM2,5 in the Xidi Village (China) during spring and winter season and suggests the use of predictive models to adjust travel plans of tourists. Climate changes can of course either increase or decrease the hazy weather conditions, and therefore should be taken into great consideration when applying the model. Please consider the suggestion of the author for your next vacations!Going from China to India, Keerthana and Nair (2025) propose a very complete and complex study aimed at modelling and forecasting groundwater levels of a tropical river basin in Kerala. The authors consider many possible parameters affecting the level of groundwater sources, including, temperature, and therefore make a direct reference to global warming consequences. The paper is worth reading, as the proposed approach could be applied for many other similar cases.The third paper remains in India although much more to the North. It still deals with water, and seasonal changes, but this time the authors (Sharma et al., 2025) look at a river water and are more interested in the water quality than in the water quantity. Non-etheless, once more, they observe that the change in climate conditions affect the natural quality of surface water sources, as already observed by other authors in totally different parts of the world (Dawe, 2006). The presented analysis of seasonal variations of several different pollutants is very detailed, and the readers may find a source of inspiration, in this paper, to conduct a monitor campaign to observe what’s going on in their own country in terms of river water quality seasonal changes, and what can be done to reduce the pollution level.The fourth paper goes from India to the East of Azerbaijan. The study proposed by Dashti et al. (2025) is fully centered on the consequences of climate changes, and on the importance of creating a climate-smart agriculture. The authors, indeed, present an original study aimed at finding the parameters which mainly affect the climate-smart variables, and conclude that infrastructural, organizational, educational, technical, cultural, legal, social and economic factors are all important for the development of climate-smart agriculture. So read it accurately, as climate-smart agriculture is the future for our food, and we need to be resilient to climate changes in any sectors (Webb, 2022).One having approached the Easter part of Europe, this issue cross-es the whole old continent arriving at the very western part of it, with the paper presented by Tufail et al. (2025) which analyses the influence of the characteristics of the Azores High on surface climate. The study in centered on a case study and allows to better understand the effect of climate changes in peninsular Spain. A must read, not only for Spanish people!The last paper does not have a geographic characterisation in its content, and is not directly related to climate changes, but deals with a very appealing topic, which is the development of sustainable adsorbent materials (Rafati et al., 2025). I do not want to add any other details, concerning the material, because you have to read it and discover by yourself its interest.So, please start immediately reading the whole issue, I’m sure you will like it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".