Spawning Habitat Unsuitability: An Impediment to Cisco Rehabilitation in Lake Michigan?
Bibliographic record
Abstract
Abstract The cisco Coregonus artedi was one of the most important native prey fishes in Lake Michigan and in the other four Laurentian Great Lakes. Most of the cisco spawning in Lake Michigan was believed to have occurred in Green Bay. The cisco population in Lake Michigan collapsed during the 1950s, and the collapse was attributed in part to habitat degradation within Green Bay. Winter water quality surveys of lower Green Bay during the 1950s and 1960s indicated that the bottom dissolved oxygen (DO) concentration was less than 2 mg/L throughout much of the lower bay, and most cisco eggs would not successfully hatch at such low DO concentrations. To determine present-day spawning habitat suitability in lower Green Bay, we compared cisco egg survival in lower Green Bay with survival at a reference site (St. Marys River, Michigan–Ontario) during 2009. We also conducted winter water quality surveys in lower Green Bay and the St. Marys River during 2009 and 2010. Cisco egg survival in lower Green Bay averaged 65.3%, which was remarkably similar to and not significantly different from the mean at the St. Marys River site (64.0%). Moreover, the lowest bottom DO concentrations recorded during the winter surveys were 11.2 mg/L in lower Green Bay and 12.7 mg/L in the St. Marys River. These relatively high DO concentrations would not be expected to have any negative effect on cisco egg survival. We conclude that winter water quality conditions in lower Green Bay were suitable for successful hatching of cisco eggs and that water quality during the egg incubation period did not represent an impediment to cisco rehabilitation in Lake Michigan. Our approach to determining spawning habitat suitability for coregonids would be applicable to other aquatic systems. Received May 14, 2011; accepted July 6, 2011
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".