A simulation-based comparison of confidence interval coverage, bias, and variance of alternative spatial biomass estimation methods used for the evaluation of Northern Shrimp biomass
Bibliographic record
Abstract
In fisheries management, reliable estimates of population abundance metrics such as standing biomass are crucial for sustainability and balanced decision-making. The OGive MAPping (OGMAP) method, used by the Department of Fisheries and Oceans (DFO) in Newfoundland and Labrador for biomass estimation, addresses non-normally distributed populations but raises concerns about handling spatial data variations. I conducted a simulation-based comparative analysis comparing OGMAP against Generalized Additive Models (GAMs) and STRAtified Programs (STRAP) to answer the following question: “are the uncertainties of the estimates calculated from these different methods reliable?” \nUsing Northern Shrimp, Pandalus borealis, as a reference, I simulated biomass landscapes, exploring parameters like landscape roughness, sampling intensity, and model settings. The analysis consistently showed OGMAP's failure to capture nominal confidence intervals (CIs) compared to alternatives, regardless of the treatment. OGMAP exhibited tighter intervals, raising concerns about overfitting and its inability to reflect the true landscape biomass. However, halving the automatically optimized bandwidths for OGMAP's probability distribution fields significantly improved its realized coverage. \nThese findings underscore OGMAP's variability, shedding light on its limitations in decision-making by the Department of Fisheries and Oceans. I stress the pivotal role of reliable estimates in fisheries management. Additionally, I suggest that alternative methods, like GAMs, may offer more dependable forecasts given OGMAP's underperformance. This research prompts a review of the fisheries management framework relying on OGMAP, suggesting potential inadequacies in capturing the true uncertainty associated with spatially distributed stocks.
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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 | 0.051 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".