Issue Information
Bibliographic record
Abstract
Aims and Scope• Fisheries Management and Ecology is a journal that aims to serve as a forum for applied studies and reviews of fishery management and ecology from across the world.Studies can span from small-scale artisanal fisheries to large-scale industrial fisheries in developing and developed countries.Studies of fishery management practices (harvest, habitat, or population manipulations), effects of fish stocking on management of fisheries, ecology and population dynamics of fish stocks that support fisheries, and fish stock assessments and associated methods are invited, if they are presented in an appropriately broad geographical, biological, or methodological scope to be of interest and utility for an international audience of fishery managers and ecologists. The Journal aims to:• foster an understanding of the maintenance, development, and management of conditions under which fish populations and communities thrive, to conserve and enhance fish and their habitat; • promote understanding of the duality of fisheries as valuable recreational and commercial resources, and as pivotal indicators of aquatic habitat quality and conservation status; • facilitate the study and understanding of policy, management, operational, conservation, and ecological issues related to sustainable management of fisheries; • increase awareness of ecologists to the needs of fisheries managers for information, techniques, tools, and concepts useful for managing fisheries; • integrate ecological studies with all aspects of fisheries management;• ensure that conservation of fisheries and their environments is a recurring theme in management of fisheries and aquatic ecosystems.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.827 | 0.727 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".