Using geostatistics to quantify seasonal distribution and aggregation patterns of fishes: an example of Atlantic cod (<i>Gadus morhua</i>)
Bibliographic record
Abstract
Geostatistical methods were used to (i) quantify fish aggregation patterns over a range of scales (100 m to 67 km) using both simulated and acoustic density data of Atlantic cod (Gadus morhua) and (ii) examine how changes in aggregation patterns influenced the precision of geostatistical density indices. Variogram parameters (range, sill, and nugget) reflected changes in distribution patterns. Variograms of dispersed and low-density aggregations had large range and small sill and nugget values. In contrast, when fish were aggregated in a small portion of the study area, the range was low and the sill and nugget large. The precision of density indices (coefficient of variation) was below 20% in all cases but at a maximum during summer when cod were broadly distributed in small, moderate to dense aggregations. Geostatistical modeling allowed us to describe and quantify distribution patterns of fish density over different scales of observation, comparisons of spatiotemporal changes in density distribution, and estimations of the precision of density indices while accounting for the effects of heterogeneous distributions, outliers and the typically large number of zero and low-density observations. Geostatistical methods have particular applicability to fishes exhibiting gregarious behaviour and seasonally variable distributions, which include many temperate and high-latitude fish 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".