Additional file 1 of An evaluation of acoustic telemetry as a method to study the movements of Asteroidea (Asterias rubens)
Bibliographic record
Abstract
Additional file 1: Figure S1. Ambient water temperature during acclimatisation and the experimental period. The start of the experiment is marked by a vertical dashed line. Figure S2. Salinity ppt during acclimatisation and the experimental period. The start of the experiment is marked by a vertical dashed line. Figure S3. Sea star attachment methods a) Horizontal Piercing of the Central body, b) Horizontal Piercing on the Arm, and C) Vertical Piercing of the Central body. Figure S4. Posterior predictive checks that compare the observed variable y to 100 simulated datasets yrep for a) feeding rate, b) probability of injury, and c) righting time models. Figure S5. Righting times in minutes per righting attempt for each of the four Treatment levels and Basket replicates (1-4). The thickness of boxes represents the number of sea stars per righting attempt. The horizontal line indicates the median, lower, and upper hinges corresponding to the first and third quartiles (the 25th and 75th percentiles). Whiskers extend from the hinge to no further than 1.5 × the inter-quartile range. Table S1. Summary table of weekly minimum convex polygon data. n = number of detections per week. Figure S6. Area m2 of weekly sea star minimum convex polygons. Figure S7. Proportion of MCP overlap between successive weeks since release. Figure S8. Distribution of sea star detections per month for entire tracking period A) June 2021 to December 2021 or B) August 2021 to February 2022. Table S2. Parametric coefficients for model 1 (response variable step length) and model 2 (response variable speed). Table S3. Approximate significance of smooth terms for model 1 (response variable step length) and model 2 (response variable speed).
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.830 | 0.154 |
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".