GIS Layer of Standardized Fish Mercury Concentrations Across Canada Now Available
Bibliographic record
Abstract
Society of Environmental Toxicology and Chemistry North America Conference 2014 (Vancouver, Canada) Poster presentation. Poster Abstract: GIS Layer of Standardized Fish Mercury Concentrations Across Canada Now Available A national GIS layer of standardized fish mercury (Hg) data would be useful to the scientific community for many types of spatial analyses and modeling. To produce this national GIS layer for Canada, we first assembled all available fish Hg data across the country and did quality-assurance checks, which resulted in 387,872 fish Hg records. We removed all records from sites with known point-source Hg inputs or from hydroelectric reservoirs. The resulting dataset contained 231,063 records from 3547 locations across Canada from 1967-2010. We used this data and the USGS National Descriptive Model for Mercury in Fish (NDMMF) to estimate Hg concentrations in a standard-length (12-cm) whole yellow perch for each sampling location. The resulting geocoded dataset of standardized fish Hg concentrations is called the Fish Mercury Datalayer for Canada, or FIMDAC, and is now available to the scientific community. FIMDAC can be used for Hg eco-risk assessment, modeling of Hg dynamics and bioaccumulation in aquatic ecosystems, spatial analysis of Hg biogeochemistry, and as baseline for modeling future Hg management scenarios and their environmental consequences. FIMDAC is not appropriate for human health risk assessment, temporal trend analysis or for setting human consumption guidelines. Relevant links: Data request form: http://www.smu.ca/research/fish-mercury-datalayer.html Metadata: http://dx.doi.org/10.6084/m9.figshare.1210773
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".