The Last Coastal Jaguars of Ecuador: Ecology, Conservation and Management Implications
Bibliographic record
Abstract
Ecuador is one of the top countries with the highest biodiversity indexes in the planet. Among the mammal species inhabiting tropical forests along Ecuador’s coast, wild cats such as ocelots (Leopardus pardalis), jaguarundis (Puma yagouaroundi), cougars (Puma concolor) and jaguars (Panthera onca) are a key group of carnivores deserving critical consideration because these species are facing several anthropogenic threats and conservation challenges. Of particularly attention is the critically endangered subspecies of jaguar (Panthera onca centralis) from the Ecuadorian coast. Despite this species is the largest cat in Ecuador’s coastal tropical forests and demanding large territories to survive, little is known about its population and conservation status. In most forests along Ecuador’s coast, habitat loss due to deforestation and fragmentation, poaching of prey and illegal hunting threaten the survival of jaguars and questions linger about its ecology and population health. Based on recent field observations using transects and deployment of camera traps, as well as surveys conducted with the local community in and around Cerro Blanco Protected Forest and surrounding areas of the Cordillera Chongón-Colonche Mountain Range, we advance the state of the ecological knowledge of coastal jaguar populations with conservation implications of its threatened habitat and long-term survival in Ecuador.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".