Scaling up experimental trawl impact results to fishery management scales — a modelling approach for a “hot time”
Bibliographic record
Abstract
Numerous studies have quantified trawl impacts at small scales. However, effective management of trawl impacts requires synthesis of experimental results (biomass depletion per tow and subsequent recovery) and application at fishery scales — realistically, this is achievable only in a modelling framework. We present a method for scaling up experimental results for management applications that incorporates a benthic biomass model having exponential trawl depletion and logistic recovery. Ultra-fine trawl-track data, supported by simulations, show that realistic trawling can be represented by a negative-binomial stochastic process, with intensity governed by large-scale effort and aggregation by a tunable parameter. Two mechanisms of the process are considered: aggregations in space (hot spots) and aggregations in time (hot times), which yields a logistic differential equation for the large-scale biomass over time. The model shows that scaling from fine scale to fishery scale depends on the degree of aggregation of fishing, with increasing aggregation lowering depletion rates at fishery scales. This model is a fundamental step in enabling assessment of large-scale implications and evaluating alternative management strategies.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".