Additional file 2 of Wearable reproductive trackers: quantifying a key life history event remotely
Bibliographic record
Abstract
Additional file 2: Additional plots and tables to support results section. Figure S2. Quantile values from training data. Quantile values extracted from the training set of known breeders for incubation and non-incubation days during the breeding season. The daily values were pooled across all individuals and the 97.5th and 2.5th quantiles calculated for incubation and non-incubation days. The classification and scores in brackets relate to how individuals with unknown breeding status were scored in the classifier. Table S1. Precision and recall values: joint classifier 3s burst. The precision and recall values when classifying avian incubation events using a joint classifier across a variety of sampling schedules for accelerometer data (ODBA interval) and GPS data (GPS interval). The results are shown for a 3 s accelerometer burst. Darker shading indicates larger values and, therefore, a more accurate classification. Table S2. Precision and recall values: joint classifier 2s burst. The precision and recall values when classifying avian incubation events using a joint classifier across a variety of sampling schedules for accelerometer data (ODBA interval) and GPS data (GPS interval). The results are shown for a 2 s accelerometer burst. Darker shading indicates larger values and, therefore, a more accurate classification. Table S3. Precision and recall values: joint classifier 1s burst. The precision and recall values when classifying avian incubation events using a joint classifier across a variety of sampling schedules for accelerometer data (ODBA interval) and GPS data (GPS interval). The results are shown for a 1 s accelerometer burst. Darker shading indicates larger values and, therefore, a more accurate classification. Table S4. Additional incubation form joint classifier. The number of additional incubation classified, in comparison to the reference incubations, using a joint classifier across a variety of sampling schedules for accelerometer data (ODBA interval) and GPS data (GPS interval). The results are shown for a 3, 2 and 1 s accelerometer bursts. Darker shading indicates larger values and, therefore, a more additional incubations. Table S5. Average days misclassified. The average number of days misclassified per individual breeding season, in comparison to the reference incubations, using a joint classifier across a variety of sampling schedules for accelerometer data (ODBA interval) and GPS data (GPS interval). The results are grouped by the outcome of the individual breeding season in the reference sample, i.e., ‘1–5 days’ is a 1–5 day incubation in the reference sample. Darker shading indicates larger values and, therefore, a greater misclassification rate. Table S6. Precision and recall GPS only classifier. The precision and recall values when classifying avian incubation events using a GPS only classifier across a variety of sampling schedules for the GPS data (GPS interval). Darker shading indicates larger values and, therefore, a more accurate classification. Table S7. Precision and recall ACC only classifier. The precision and recall values when classifying avian incubation events using an Accelerometer only classifier across a variety of sampling schedules for the ODBA data (ODBA interval) and length of the accelerometer burst (Burst length). Darker shading indicates larger values and, therefore, a more accurate classification.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.714 | 0.147 |
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".