Predicting mercury concentrations and fluxes in the water column and sediment of lakes with a limited dataset
Bibliographic record
Abstract
The purpose of the present study was to evaluate the mercury (Hg) Environmental Ratios Multimedia Ecosystem Sources (HERMES) model on two Ontario, Canada lakes (Harp and Dickie) and to include modifications to enable the model to estimate the major model input variables that tend to be missing for lakes with limited datasets. No significant differences were found for either sediment solid or bulk water total mercury (THg) when the HERMES model was applied to the two Ontario lakes, regardless of whether all available data were altered during application or only the 10 variables that tend to cause the most variation in model output (i.e., concentration of THg in atmosphere, water inflow THg concentration, water inflow rate, water volume, surface area, mean depth, suspended particulate matter concentration, settling rate of solids in water column, water temperature, and precipitation rate). Since measured sediment and water THg values do not exist for most lakes removed from industrial activities, empirical relationships were incorporated into the HERMES model framework to provide a method to double-check model output for lakes where this information is unavailable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".