Metadata: Fish Mercury Datalayer for Canada (FIDMAC)
Bibliographic record
Abstract
Metadata: Fish Mercury Datalayer for Canada (FIDMAC). Excerpt: SUMMARY Until now, characterization of mercury (Hg) risks posed to piscivorous fish and wildlife through the consumption of prey fish has generally remained limited to local or regional surveys. Furthermore, spatiotemporal and sample characteristic effects in fish-mercury data can lead to difficulty comparing results from different studies. The Fish Mercury Datalayer for Canada (FIMDAC, Depew et al. 2013a) provides model-derived estimates of Hg in a common indicator species (12-cm whole-yellow perch), and represents a useful preliminary national-level standardized index of Hg exposure to piscivorous fish and wildlife. DESCRIPTION The FIMDAC represents a model-derived output of Hg concentrations in a common indicator species (12-cm whole-yellow perch), established from the application of the United States Geological Survey's (USGS) National Descriptive Model of Mercury in Fish (NDMMF, Wente 2004) to the Canadian Fish Mercury Database (CFMD, Depew et al. 2013b). The geographical distribution of yellow perch is wide-ranging, and they represent an important prey species for piscivorous fish, birds, and mammals. Parameters estimated by way of NDMMF were unbiased, and strong spatial biases in prediction error were not evident. The FIMDAC records represent the estimated Hg burden (ug.g, wet weight) for a standard length (12 cm) whole-yellow perch at 1936 unique freshwater sites across Canada, collected between 1990 and 2010. Further details regarding the development of the FIMDAC can be found in Depew et al. (2013a).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.326 | 0.163 |
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".