The association between watershed characteristics and mercury concentrations in fish of Northern Ontario lakes
Bibliographic record
Abstract
Many landscape, limnological, and ecological factors synergistically affect the mercury cycle and subsequently influence total mercury (THg) concentrations in fish. In Chapter 1, the associations between watershed and lake scale characteristics with THg in piscivorous fish are examined. ArcGIS was used to delineate the waterbody catchment area and extract waterbody catchment characteristics for 243 of northern Ontario?s lakes. Walleye (Sander vitreus, n= 121 lakes), lake trout (Salvelinus namaycush, n= 60 lakes), brook trout (Salvelinus fontinalis, n= 18 lakes), northern pike (Esox lucius, n =107 lakes), and smallmouth bass (Micropterus dolomieu, n = 37 lakes) were standardized to the mean length of the populations by using power-series regressions. Multivariate analysis (non-metric multidimensional scaling) and univariate analysis were used to determine the associations between total mercury concentrations in fish and watershed scale and lake scale variables. Watershed and lake chemistry characteristics poorly described the variability in THg concentrations. Forest harvesting and natural disturbance were not associated with fish mercury concentrations. \nIn Chapter 2, the relationship between walleye (Sander vitreus) growth rates and mercury concentrations was evaluated. The von Bertalanffy growth model was used to standardize the age of walleye to the mean total length. Walleye populations with slower growth rates had higher THg concentrations (r2=0.333, p< 0.001), suggestive of growth efficiency. Moreover, abundance of walleyes was associated with the growth rate (r2 =0.136, p<0.0001). \nConcentrations of THg in piscivorous fish are attributed to physical, chemical, and ecological characteristics of lakes. It is likely that lake ecology exerts the strongest influence on high mercury concentrations in piscivorous species, masking the effect from from watershed disturbance.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".