Session Five: Contaminants and Ecosystem Health MATHEMATICAL MODELS TO ESTIMATE THE FRESHWATER AND CONTAMINATION DISCHARGE TO COASTAL AREAS DUE TO ANTHROPOGENIC ACTIVITIES
Bibliographic record
Abstract
Virtually every surface water in coastal watersheds, including rivers, lakes, wetlands, and estu-aries, interacts with adjacent subsurface water. This interaction affects the water quality and quantity in both surface water and subsurface water. Subsurface water and surface water interaction affects chem-istry, especially acidity, temperature, dissolved solids oxygen, and reduction-oxidation potential. As land and water resource development increases in coastal watersheds, it is becoming readily apparent that subsurface water and surface water interaction must be considered in establishing water manage-ment policies. This interaction can take many forms, but the most common interactions are between subsurface water and stream water, lakes, and wetlands. In coastal areas, interactions between subsur-face water and seawater occur. All these interactions occur in the Bay of Fundy and other coastal areas of Canada. There are a wide range of reasons and applications for the study of subsurface water relation-ships in the coastal zone. This diversity, in combination with the range of disciplines and the time and space scales involved, complicate the use of data for purposes other than those envisioned by the original investigator. The challenge is particularly great in the case of local type or case studies de-signed for global or regional extrapolation since errors or inappropriate assumptions will be greatly magnified. Mathematical models have a wider range of application and are the concern of this paper. Due to the complex nature of the problem, each of these mathematical models is based on certain simplifying assumptions and approximations. This paper examines the approaches of different types of numerical models, and presents the results of application of models to two different sites.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".