Relationship between the average annual temperature and the area of Amazonian humid forest in the departments of Peru, 2013-2021
Bibliographic record
Abstract
The present study analyzed the relationship between the average annual temperature and the area of Amazonian forest in the departments of Peru during the period 2013-2021, using a panel data model with random effects. The data used come from the National Institute of Statistics and Informatics (INEI) and include the average annual temperature in degrees Celsius and the area of Amazonian rainforest in thousands of hectares, both disaggregated by department. Additionally, CO2 emissions resulting from the loss of tree cover, measured in megatons (Mt) of carbon dioxide equivalent (CO₂e), were considered as a control variable. The results revealed a positive and statistically significant relationship between the area of Amazonian forest and the average annual temperature, denoting that an increase of one thousand hectares in the extension of the forest corresponds to an increase of 0.0004 °C in temperature. In this sense, the finding contradicts the climate-regulating role played by forests, however, this is attributed to the influence of unobserved confounding variables that are linked to both forest area and temperature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".