What are the impacts of small-scale dredging activities on inland fisheries productivity? A systematic review protocol
Bibliographic record
Abstract
Small-scale dredging activities in freshwater bodies have the potential to impact habitats and food resources that fishes depend on, and ultimately impact fisheries productivity. This systematic review will explore the evidence base for small-scale dredging impacts on the indicators of fisheries productivity, and will help to inform management decisions that seek to reconcile biodiversity conservation and freshwater fisheries, with potentially disruptive anthropogenic activities in freshwater environments. This systematic review will examine, summarize and synthesize all available evidence on the impacts of small-scale dredging activities on surrogate indicators of fisheries productivity. All studies in freshwater habitats in temperate regions in both the Northern and Southern hemispheres will be considered. Both peer reviewed primary and grey literature will be included in the review, and searches will be conducted in academic journal databases, online search engines, and specialist websites. Study validity will be critically assessed to identify any risk of bias. Data will be presented as a narrative synthesis, and if sufficient good quality data are available, a meta-analysis will be performed.
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 | 0.086 | 0.096 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".