Assessing the Development of Shorebird Eggs Using the Flotation Method: Species-Specific and Generalized Regression Models
Bibliographic record
Abstract
Abstract Abstract We modeled the relationship between egg flotation and age of a developing embryo for 24 species of shorebirds. For 21 species, we used regression analyses to estimate hatching date by modeling egg angle and float height, measured as continuous variables, against embryo age. For eggs early in incubation, we used linear regression analyses to predict hatching date from logit-transformed egg angles only. For late incubation, we used multiple regression analyses to predict hatching date from both egg angles and float heights. In 30 of 36 cases, these equations estimated hatching date to within four days of the true hatching date for each species. After controlling for incubation duration and egg size, flotation patterns did not differ between shorebirds grouped by mass (≥100 g or <100 g) or taxonomy (Scolopacidae versus Charadriidae). Flotation progressed more rapidly in species in which both adults incubate the clutch versus species in which only one adult incubates the clutch, although this did not affect prediction accuracy. We also pooled all continuous data and created a generalized regression equation that can be applied to all shorebird species. For the remaining three species, we estimated hatching date using five float categories. Estimates of hatching date using categorical data were, overall, less accurate than those generated using continuous data (by 3%–5% of a given incubation period). Our equations were less accurate than results reported in similar studies; data collected by multiple observers and at multiple sites, as well as low sample sizes for some species, likely increased measurement error. To minimize flotation method prediction error, we recommend sampling in early incubation, collecting both egg angle and float height data in late incubation, and developing site- and species-specific regression models where possible.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".