FIDMAC Standardized Yellow Perch Mercury Dataset for Canada
Bibliographic record
Abstract
Excel spreadsheet for Fish Mercury Datalayer for Canada (FIMDAC). 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. Please read the metadata file first before using. Metadata: Fish Mercury Datalayer for Canada (FIDMAC). DOI: http://dx.doi.org/10.6084/m9.figshare.1210773. DC Depew, NM Burgess & LM Campbell. 2013. Modelling mercury concentrations in prey fish: Derivation of a national-scale common indicator of dietary mercury exposure for piscivorus fish and wildlife. Environmental Pollution 176:234-243. http://dx.doi.org/10.1016/j.envpol.2013.01.024.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.045 |
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".