Analysis of Cisco (Coregonus artedi) Populations in Eastern Lake Ontario
Bibliographic record
Abstract
The Cisco (Coregonus artedi) is a cold-water planktivorous fish species that has undergone significant declines within Lake Ontario as a result of over-exploitation and other anthropogenic impacts. The goal of this project was to expand upon the current scientific knowledge base for Cisco within Eastern Lake Ontario [ELO] by providing an up-to-date assessment of the current status of this population. This thesis focusses on two distinct topics, each related to a distinct natural history characteristic of the ELO Cisco population that could represent a factor limiting population recovery in this region: fisheries-induced alterations to life-history traits, and fluctuations in year-class strength. In order to identify whether fisheries-induced change has occurred in this population, I compared the size-at-age and maturation of the ELO population both before [1926-1928] and after [1992-2016] the collapse of the fishery. The modern population had larger body sizes at a given age compared to the historical group, as well as a lower age-at-maturity [size-at-maturity remained similar]. It appears as though Cisco within the historical population may have been experiencing fisheries-induced selection in size-at-age, with potential recovery of this trait occurring in the modern population after a reduction in fishing pressure. This suggests that Cisco have a high capacity for adaptation through either phenotypic plasticity or evolutionary change. To assess patterns in year-class strength [YCS] within the ELO population, I used a residual catch-curve method to first calculate relative YCS, and then used linear models to correlate any fluctuations in YCS with environmental variables [temperature, ice cover, wind velocity]. The ELO population of Cisco demonstrated high variability in YCS, and only the timing of first consistent ice cover appeared to be correlated with these fluctuations. The environmental conditions influencing YCS in Lake Ontario may be different from those acting in other Great Lakes, highlighting the importance of local assessment. Advancing our knowledge of Cisco life-history within Lake Ontario provides important insights into the current condition of this population, which could have potential implications for future rehabilitation in this region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".