Adult and larval fish assemblages vary among small tributary mouths of Green Bay, Lake Michigan
Bibliographic record
Abstract
Small tributaries of the Great Lakes serve as important habitat during critical life stages of many fish species, though temporal and spatial dynamics of the assemblage that uses these systems are seldom investigated. This study quantifies larval and adult fish assemblages captured by fyke net and light traps among small tributary mouths of Green Bay, Lake Michigan. Ten tributaries harbored a total of 45 species representing 17 families, with the most abundant including spottail shiner (Notropis hudsonius (Clinton, 1824)) in adult assemblages and white sucker (Catostomus commersonii (Lacepède, 1803)) in larval assemblages. Larval fish assemblage structures differed over five biweekly sampling events in May and June. Adult fish assemblage structures varied among tributaries but not among spring, summer, and fall samples. Larval and adult species assemblages at these river mouths are likely influenced by hydrology, habitat structure, and species-specific ecology. Water movement may transport larvae into river mouths, as larval assemblages were dominated by species that spawn in coastal habitats. Adult species richness varied with longitude, with the greatest diversity in tributaries on the west shore. This investigation of fish assemblages highlights the spatial and temporal variation that occurs in these systems and their role in shaping fish populations in Green Bay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".