Development of a collaborative long-term fish community assessment in the St. Clair-Detroit River System
Bibliographic record
Abstract
Fish community assessments in large systems such as the Laurentian Great Lakes and their connecting channels are difficult to conduct due to their size, complex habitats, need for specialized sampling gear, and often harsh conditions. However, these assessments are necessary to document population trends and support sustainable management. Given the challenges of conducting fish community surveys in these systems and their interjurisdictional management, collaborative assessments are often necessary to obtain the data needed to manage shared resources. In the St. Clair-Detroit River System, the Michigan Department of Natural Resources , Ontario Ministry of Natural Resources , and U.S. Fish and Wildlife Service (USFWS) collaborated on designing and implementing a long-term coordinated fish community assessment that met data needs for management of fisheries while contributing to surveillance efforts of the USFWS non-native species early detection and monitoring program. The assessment consists of several surveys that are conducted on a rotational basis, including a small-mesh fyke net survey, gill net survey, and nearshore electrofishing survey on Lake St. Clair along with multi-gear (bottom trawls, electrofishing, and gill nets) surveys on the Detroit River and St. Clair River. The coordinated assessment began in 2021 and has benefited each agency, allowing for increased spatial coverage, sampling effort, and return on investment (i.e., more data collected for the time and money spent by each agency). Over the long term, this assessment can be used to evaluate changes in the fish community and impacts of future environmental issues , and provide opportunities for future research and collaboration.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".