Influence of shoreline features on fish distribution in the Laurentian Great Lakes
Bibliographic record
Abstract
In this paper, we used assembled fish distributions (over 9500 field observations) and correlated them with 11 categories of the Great Lakes shoreline (i.e., bedrock, bluff, coarse beach, sandy beach dune, sandysilty bank, clay bank, low riverine coastal plain, composite, wetland, artificial, and unclassified) to validate the appropriateness of classifying Great Lakes fishes into three species complexes (taxocenes) that account for differences in their dependence on shoreline features. A χ2 goodness-of-fit test with Bonferroni correction indicated a significant positive association between the presence of fish and three shoreline classes: wetland, sandy beach dune, and bluff. The Dutilleul modified t test was used to quantify the correlation between wetlands and distribution of the 25 most abundant species and those of different functional groupings. Our results confirm that (i) the Great Lakes fish community utilizes certain shoreline features (especially wetlands) disproportionately to their availability, (ii) the distribution of wetland-associated taxa is influenced by wetland type (i.e., protected embayment versus open-shoreline wetland), and (iii) the preferred utilization of coastal wetlands by a majority of the fish community is consistent across geographical scales, from the local site to the entire Great Lakes shoreline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".