Aerial photographic identification of narwhal (<i>Monodon monoceros</i>) newborns and their spatial proximity to the nearest adult female
Bibliographic record
Abstract
Population and species management of long-lived species such as narwhal (Monodon monoceros) require long-term ecological monitoring programs to provide baseline information on population structure and dynamics. The success of such programs is dependent on the repeatability of the methods. Here, we propose a dichotomous key to identify narwhal newborns from aerial photography based on cetaceans’ mother–newborn dyad behavioral and narwhal newborn physical description. The key was tested by three inexperienced observers and one expert observer with interobserver agreement classified as fair according to the Cohen Kappa algorithm and criteria thresholds. This study gives some insight into narwhal-newborn spatial position, showing a predominant number of newborns located in the infant and echelon position.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".