Investigating Co-occurrence among Look-alike Species: The Case of Three Bears in Northeast India
Bibliographic record
Abstract
At the most basic level, the assessment of a species’ status involves knowing where it occurs. Determining the presence of rare species is difficult, and can be further confounded by the presence of a more common look-alike species. We investigated one of the few places in the world where three species of bears have been reported to co-occur at a fine scale: Balpakram National Park, Meghalaya, India. Asiatic black bears (Ursus thibetanus) are fairly common, and we sought to determine whether sun bears (Helarctos malayanus) and/or sloth bears (Melursus ursinus) also resided there. The local Garo language has words for three types of bears, and some local people reported the continued presence of a small type of bear, possibly the sun bear, but the probable extirpation of sloth bears. Because these bears look somewhat alike, local people and government forest officers could not provide convincing accounts of the presence of more than one species. We measured claw marks on climbed trees, a method used to differentiate sun bears from Asiatic black bears where both are known to occur; however, this method turned out to be unreliable for detecting sun bears where their presence was unknown because sun bear-sized marks are not distinguishable from juvenile black bears. We recommend targeted camera trapping near recent purported sightings of the other two bear species.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".