Using Digital Photographs to Evaluate the Effectiveness of Plover Egg Crypsis
Bibliographic record
Abstract
Abstract: The focus of digital photography has moved from documentation to quantitative analysis. To illustrate the potential application of this diagnostic tool to quantify color and shape, we photographed both artificial and natural semipalmated plover ( Charadrius semipalmatus ) nests to determine what benefits, if any, were derived from egg crypsis (i.e., eggshell color and egg marking shape). This simple and cost‐effective method provides precise and repeatable quantification of color and shape that discriminated subtle differences in egg crypsis of artificial and natural nests that were not visible to us. The advantages of digital photography and image‐editing software outweigh any shortcomings, as long as standard protocols are followed for capturing and analyzing images. Used with due care, digital photography is useful in studies that need to quickly assign biological traits, such as age and sex, to individuals, and to document temporal change that may occur as a response to management or disturbance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".