The effectiveness of spawning habitat creation or enhancement for substrate spawning temperate fish: a systematic review protocol
Bibliographic record
Abstract
Abstract Background Habitat is the foundation for healthy and productive fisheries. For substrate spawning fish, lack of appropriate spawning substrate is inherently limiting and a lack of access to suitable spawning habitat will lead to population collapse. When specific properties of a habitat (e.g., temperature, depth, vegetation composition) are matched to the species’ ecological niche, a spawning habitat can be created or enhanced as a means of mitigating or offsetting the harmful effects of human development. Given the acceleration of habitat degradation in aquatic systems as a result of human activity and resultant loss of biodiversity, it is becoming ever more important to consider the effectiveness of the techniques being used to enhance or create habitat, to ensure management resources are being allocated wisely. The primary aim of this systematic review will be to assess the effectiveness of techniques currently being used to create or enhance spawning habitat for substrate spawning fish in temperate climate regions. Methods This review will examine studies on the effectiveness of habitat creation or enhancement for substrate spawning fish. We will consider studies in either the North or South temperate climate regions, and include freshwater, estuarine, coastal, or marine environments. Relevant outcomes will include a range of measures used by authors to define effectiveness, including but not limited to the presence of eggs, successful emergence, or improved recruitment. This review will obtain relevant studies from online publication databases, specialist websites, and grey literature using a range of search engines and networking tools. Additional searches will be conducted using the bibliographies of relevant review publications. Study data will be extracted and appraised for quality, including study design, confounding factors, and statistical analysis. A narrative synthesis will be compiled and a meta-analysis will be completed should the data availability and quality allow for it.
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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 | 0.070 | 0.095 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.014 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 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".