Heat remains unaccounted for in thermal physiology and climate change research
Bibliographic record
Abstract
In the aftermath of the Paris Agreement, there is a crucial need for scientists in both thermal physiology and climate change research to develop the integrated approaches necessary to evaluate the health, economic, technological, social, and cultural impacts of 1.5°C warming. Our aim was to explore the fidelity of remote temperature measurements for quantitatively identifying the continuous redistribution of heat within both the Earth and the human body. Not accounting for the regional distribution of warming and heat storage patterns can undermine the results of thermal physiology and climate change research. These concepts are discussed herein using two parallel examples: the so-called slowdown of the Earth's surface temperature warming in the period 1998-2013; and the controversial results in thermal physiology, arising from relying heavily on core temperature measurements. In total, the concept of heat is of major importance for the integrity of systems, such as the Earth and human body. At present, our understanding about the interplay of key factors modulating the heat distribution on the surface of the Earth and in the human body remains incomplete. Identifying and accounting for the interconnections among these factors will be instrumental in improving the accuracy of both climate models and health guidelines.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".