Estimating nestling diet with cameras: quantifying uncertainty from unidentified food items
Bibliographic record
Abstract
Cameras at nest sites are becoming a common means for quantifying nestling diet, but there are two problems associated with this method: food items delivered to nestlings often cannot be identified, and quantification of error around diet estimates for individual nests is problematic. We present a novel method of incorporating unidentified food items into diet estimates and quantifying error around these estimates for individual nests. In our method, unidentified food items are accounted for by considering all of the possible ways in which they could be allocated among previously defined food categories (possible outcomes). We then calculate the probability of each possible outcome by assuming the probability that an unidentified food item belongs to any given category is equal to the proportion of identified items from that category. All possible outcomes, along with the probability of each, represent a probability space. We allocate the unidentified food items to each category according to the most probable outcome in the probability space when estimating the contribution of each food category to nestling diets. Confidence intervals around diet estimates for each food category are estimated by simulating many samples from this probability space and using kernel density estimation. We demonstrate the implementation of our method with data from motion‐sensitive cameras monitoring Arctic peregrine falcon Falco peregrinus tundrius nests in Nunavut, Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".