DISPLACE: a dynamic, individual-based model for spatial fishing planning and effort displacement — integrating underlying fish population models
Bibliographic record
Abstract
We previously developed an individual-based model evaluating the bio-economic efficiency of fishing vessel movements from recent high resolution spatial fishery data. The assumption was constant underlying resource availability. Now, an advanced version considers the underlying size-based dynamics of the targeted stocks for Danish and German vessels harvesting the North Sea and Baltic Sea fish stocks. The stochastic fishing process is specific to the vessel catching power and to the encountered population abundances, based on disaggregated research survey data. The impact of the effort displacement on the fish stocks and the vessels’ economic consequences were evaluated by simulating individual choices of vessel speed, fishing grounds, and ports. Some scenarios led to increased energy efficiency and profit while others such as fishing closures or fishermen optimization sometimes lowered the revenue by altering the spatiotemporal effort allocation. On an individual scale, the simulations led to gains and losses due to either the interactions between vessels or to the alteration of individual patterns. We demonstrate that integrating the spatial activity of vessels and fish abundance dynamics allow for more realistic predictions of fishermen behaviour, profits, and stock abundance.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".