Large‐bodied fish assemblage characteristics in large rivers across Ontario
Bibliographic record
Abstract
Abstract Large‐bodied fish assemblages were assessed in large rivers across Ontario. The objectives of this study were to (i) determine if there were relationships in large‐bodied fish species within rivers; (ii) determine what factors explain the variability in the abundance of large‐bodied fish within rivers across Ontario; and (iii) assessed variation in large‐bodied fish biodiversity among these river. Standardized index netting was conducted at 22 sites across 12 major rivers and sampled 3889 fish representing 26 species. Species associations were evident based on correspondence analysis. Walleye, Common White Sucker, Northern Pike and Lake Whitefish formed one group; Silver Redhorse, Shorthead Redhorse and Lake Sturgeon (adult and juvenile) formed another; Burbot, Longnose Sucker and Sauger were closely associated; and Cisco, Yellow Perch, Rock Bass, Smallmouth Bass and Brown Bullhead grouped. Canonical correspondence analysis was conducted to link species abundance patterns to environmental conditions. Walleye, Common White Sucker, Lake Whitefish and Smallmouth Bass were ubiquitous. Northern Pike abundance was negatively correlated with river discharge and longitude. Burbot, Sauger and Longnose Sucker abundance were positively correlated with deep rivers and discharge. Whereas Cisco, Yellow Perch and Rock Bass abundance were greater in wider rivers with lower discharge. Lake Sturgeon (adult and juvenile), Silver Redhorse and Shorthead Redhorse abundance were greater in narrow, longer rivers. Juvenile Lake Sturgeon abundance was positively correlated with longitude and river discharge. Mean species diversity and richness of large‐bodied fish among all sites was 1.58 (0.36 SD) and 7.7 (2.6 SD). Species diversity was not significantly related to any of the variables used in the Generalized Linear Model; however, species richness was significantly related to maximum depth. This study demonstrated subtle differences in environmental variables affecting large‐bodied fish at the landscape scale rather than those observed at the river scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".