Secondary forest is utilized by Great Curassows (<i>Crax rubra</i>) and Great Tinamous (<i>Tinamus major</i>) in the absence of hunting
Bibliographic record
Abstract
Deforestation and hunting are the leading human-driven disturbances causing population declines of the vulnerable Great Curassow (Crax rubra) and the near threatened Great Tinamou (Tinamus major). These threats typically co-occur, with synergistic effects. We investigated habitat use of Great Curassows and Great Tinamous in the Matapalo corridor of the Osa Peninsula, southwest Costa Rica, where they are not hunted, to understand whether disturbed habitats can be suitable for these species. We analyzed camera trap data from 56 locations and 5579 trapping days using occupancy modeling. We obtained 195 independent captures of Great Curassows at 33 of 56 locations (59%) and 429 independent captures of Great Tinamous at 37 of 56 locations (66%). Great Curassow occupancy did not vary with habitat type but was negatively influenced by distance from roads and by elevation; detection probability varied with habitat type. Great Tinamou occupancy probability was principally related to habitat type; primary, secondary and plantation forest areas all displayed high occupancy probabilities, but occupancy of agricultural land was low. Our work suggests that secondary-growth forests can offer valuable complementary habitat to assist in the recovery of these declining species, at least when hunting is controlled and intact forests are nearby.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".