Combining data from bottom-trawl and acoustic-trawl surveys to estimate an index of abundance for semipelagic species
Bibliographic record
Abstract
Fishery-independent surveys are useful for estimating abundance of fish populations and their spatial distribution. It is necessary in the case of semipelagic species to perform acoustic-trawl (AT) and bottom-trawl (BT) surveys to ensure that sampling encompasses both midwater and demersal components of the population. Abundance estimates from both survey types are negatively biased because of the blind zones associated with fish vertical distribution. These biases can vary spatially and temporally, resulting in confounded trends and additional variation in abundance estimates. To improve abundance estimates for semipelagic species we propose a new method for combining BT and AT survey data using environmental variables to predict the vertical overlap. Using walleye pollock (Gadus chalcogrammus) AT and BT surveys in the eastern Bering Sea as an example, we show that combined estimates provide more reliable whole water column and spatial distribution estimates than either survey can by itself. Although the combined estimates are still relative, they account for the uncertainty in the bias ratio between the two survey methods and the uncertainty associated with the extent of the water column sampled by both surveys. Our method of combining BT and AT data can be extended to other semipelagic species.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".