Additional file 1 of The fate of intracoelomic acoustic transmitters in Atlantic salmon (Salmo salar) post-smolts and wider considerations for causal factors driving tag retention and mortality in fishes
Bibliographic record
Abstract
Additional file 1: Table S1. Paper identification key for the references used in the meta regression. Table S2. Meta-regression variance components for comparing tagging and experimental parameters against tag retention and mortality rates in fishes. Paper ID was treated as a random effect in the model, while experimental number was nested within this random effect. Meta-regressions were conducted using proportional data transformed using Freeman–Tukey double-arcsine transformations. Figure S1. Forest plot depicting point estimates and the corresponding 95% confidence intervals for mortality proportions in tagged fish from journal articles collected in the meta-analysis. Individual studies are represented by a coded number (Paper ID; see Additional file 1: Table S1 for references). In some cases studies collected multiple estimates of tagging-associated mortality which are represented by ticks following the study identifier. Figure S2. Forest plot depicting point estimates and the corresponding 95% confidence intervals for tag retention proportions in tagged fish from journal articles collected in the meta-analysis. Individual studies are represented by a coded number (Paper ID; see Additional file 1: Table S1 for references). In some cases studies collected multiple estimates of tagging-associated mortality which are represented by ticks following the study identifier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.835 | 0.060 |
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".