Modalités de la déforestation dans le sud-ouest de l'État du Campeche, Mexique
Bibliographic record
Abstract
The analysis of satellite images shows an important reduction of forest cover in the Lagoon of Términos region in the State of Campeche (southeastern Mexico) over the last decades. Deforestation rates reached 2.2 and 5.3%, respectively, on a yearly basis during 19741986 and 19861991. The deforestation process was modelled using a geographic information system. The model allows to determine how elements such as roads or human settlements proximity, land tenure, shape of the forest patches, slope, soil type, and human population attributes have an impact on the deforestation process. Deforestation was more severe in opened, nonflooded areas, with fertile soil, near roads and human settlements. Human population attributes showed little influence on deforestation rates, probably because pasture lands encroachment was recognized as the main cause of forest clearing. However, the model does not highlight the root causes of this phenomena, such as government policy on settlement and subsidies for cattle ranching. Despite this limitation, it allows to generate deforestation risk assessment maps that correctly identify the forest areas most susceptible to deforestation.
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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 | 0.005 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".