Degree of egg-taking by humans determines the fate of maleo (Macrocephalon maleo) nesting grounds across Sulawesi
Bibliographic record
Abstract
Abstract The maleo (Macrocephalon maleo) of Sulawesi, Indonesia, is culturally iconic and Critically Endangered, but the causes of its decline have never been systematically analyzed nor its nesting grounds comprehensively surveyed. We visited 122 previously known and 58 previously unrecorded sites, collecting data and interviewing local people at each site. We used ordinal logistic regression to fit models with combinations of 18 different predation, habitat, and nesting ground variables to determine the strongest predictors of nesting ground success, as represented by maleo numbers. At least 56% of known nesting grounds are now inactive (abandoned), and 63% of remaining active sites host ≤ 2 pairs/day at peak season. Egg-taking by humans is the single biggest driver of maleo decline. Protecting eggs in situ predicts higher maleo numbers than protecting eggs through hatchery methods. After egg-taking, quality (not length) of the travel corridor connecting nesting ground to primary forest best predicts nesting ground success. Being inside a federally protected area is not a primary driver of success, and does not ensure persistence: 28% of federally protected nesting grounds have become inactive. Local conservation efforts protected nesting grounds 2‒3 times better than federal protection. We update the methodology for assessing nesting ground status, and recommend five measures for maleo conservation, the foremost being to protect nesting grounds from egg-taking by humans at all remaining active sites.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".