Contemporary life history characteristics of Lake Superior deepwater ciscoes
Bibliographic record
Abstract
The diversity and abundance of ciscoes has declined in the Great Lakes, with only Lake Superior retaining its original Cisco fauna; yet as a group, ciscoes remain poorly studied. We examined age and growth, sex ratios, and estimated survivorship and mortality of deepwater ciscoes (Bloater: Coregonus hoyi, Kiyi: C. kiyi, and Shortjaw Cisco: C. zenithicus) and Cisco C. artedi. All fish were captured in gill nets set >60 m in Canadian waters of Lake Superior from 2007–2009. Survivorship was higher than expected, with total annual survival rates ranging from 0.615 (Kiyi) to 0.785 (Shortjaw Cisco). This result was attributed to using otoliths to estimate ages rather than scales as done in previous investigations. Maximum ages of ciscoes exceeded 20 years with Cisco, the largest, longest-lived species, followed by Shortjaw Cisco, Bloater and Kiyi. Females dominated adult populations in all species; females were larger-at-age and had greater longevity, resulting in sex ratios skewed heavily towards females. With the exception of age and growth data, the life history characteristics that we observed were consistent with historic data from the early part of the 20th century.
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 | 0.001 |
| 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) | 0.002 | 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".