Assessment of status of white sucker (<i>Catostomus commersoni</i>) populations exposed to bleached kraft pulp mill effluent
Bibliographic record
Abstract
Credible ecological risk assessments often need to include analysis of population-level impacts. In the present study, a predictive model was developed to investigate population dynamics for white sucker (Catostomus commersoni) exposed to pulp mill effluent at a well-studied site in Jackfish Bay, Lake Superior, Canada. The model uniquely combines a Leslie population projection matrix and the logistic equation to translate changes in the fecundity and the age structure of a breeding population of white sucker exposed to pulp mill effluent to alterations in population growth rate. Application of this density-dependent population projection model requires construction of a life table for the organism of interest, a measure of carrying capacity, and an estimation of the effect of stressors on vital rates. A white sucker population existing at carrying capacity and subsequently exposed to pulp mill effluent equivalent to a documented exposure experienced during the period 1988 to 1994 in Jackfish Bay would be expected to exhibit a 34% to 51% annual decrease in recruitment during the first 5 yr of exposure and approach a population size of 71% of carrying capacity. The Jackfish Bay study site contains monitoring data for biochemical endpoints in white sucker, including circulating sex steroid concentrations, that could be combined with population modeling to utilize the model demonstrated at the Jackfish Bay study site for investigation of other white sucker populations at sites that are less data-rich.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".