Northern Madtom Use of Artificial Reefs in the St. Clair–Detroit River System
Bibliographic record
Abstract
Abstract The St. Clair and Detroit rivers historically supported abundant fish populations. However, like many river systems, these rivers have been greatly altered through the creation of navigation channels and other anthropogenic disturbances, resulting in the loss of fish and wildlife habitat and declines in native fish populations. To ameliorate this environmental degradation, artificial fish spawning reefs were constructed in the St. Clair and Detroit rivers. One native species to potentially benefit from artificial reefs is the Northern Madtom Noturus stigmosus, a small ictalurid that is listed as endangered in the state of Michigan and the province of Ontario. Between 2016 and 2018, artificial reefs and nearby control sites were sampled in the St. Clair and Detroit rivers to compare the number of Northern Madtoms. In total, 171 Northern Madtoms were captured in 1,848 minnow traps with one of four bait types: cheese, dog food, worms, or control (no bait). Baited minnow traps successfully captured Northern Madtoms in the fast-flowing, deep water of the St. Clair–Detroit River system, and catch rates were significantly higher when traps were baited with worms. The number of Northern Madtoms captured was lower in the Detroit River than in the St. Clair River and increased with increasing water temperature and turbidity. Artificial reefs constructed in the St. Clair–Detroit River system are providing habitat for Northern Madtoms; however, use did not differ between reef sites and nearby control sites. This work provides insight regarding sampling strategies to target Northern Madtoms in large-river systems and highlights the importance of incorporating a temporal sampling strategy into survey design.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".