Can conservation targets for imperilled freshwater fishes and mussels be achieved by captive breeding and release programs? A systematic map protocol to determine available evidence
Bibliographic record
Abstract
Captive breeding programs are one of the many tools used by conservation practitioners as a means of conserving, supporting, and supplementing populations of imperilled species. Captive breeding programs exist around the globe for freshwater mussels and fishes, but the availability of evidence exploring the effectiveness of these programs has not yet been explored using systematic map criteria. This systematic map aims to identify, collate and describe the evidence that exists on the effectiveness of captive breeding programs, for the purpose of achieving conservation targets for imperilled freshwater fishes and mussels in the wild. The outputs of this systematic map will help to inform conservation managers and policy makers who are responsible for protecting imperilled freshwater species by identifying existing information and highlighting key information gaps for captive breeding programs operating in temperate regions. This systematic map will search for, compile, and map existing literature on the effectiveness of captive breeding programs for the conservation of imperilled freshwater fishes and mussels. The systematic map will search using five bibliographic databases, two public search engines, and 19 specialist websites and will include both primary and grey literature. All studies that discuss details related to captive breeding programs for the conservation of imperilled freshwater fishes and/or mussels in temperate regions will be included in the map. The systematic map will produce a narrative report describing the evidence, including knowledge gaps evidence clusters, and a MS-Excel searchable database of articles and extracted metadata.
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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 | 0.094 | 0.142 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.047 | 0.026 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".