Surveillance Optimization Project for Chronic Wasting Disease dataset for Ontario, Canada, 2017-2020
Bibliographic record
Abstract
This dataset contains four files containing data from the Ontario Ministry of Natural Resources and Forestry shared with the Cornell Wildlife Health Lab (CWHL) at Cornell University for the purpose of the Surveillance Optimization Project for Chronic Wasting Disease (SOP4CWD). Professionals at the source facility have provided written permission for professionals at the CWHL to post this open data to this persistent eCommons repository. OMNRF_WTD_surveillance_2020.csv: This datafile constitutes records in standardized form depicting the results of chronic wasting disease (CWD) testing of white-tailed deer (Odocoileus virginianus) in Ontario, Canada for hunting seasons from 2017-18 to 2019-20, as completed by wildlife health diagnosticians at (or in partnership with) the Ontario Ministry of Natural Resources and Forestry. OMNRF_WTD_harvest_2020.csv: This data constitutes the estimated total number of white-tailed deer (Odocoileus virginianus) legally harvested by hunters by county in Ontario, Canada for hunting seasons from 2017-18 to 2019-20, as recorded by the Ontario Ministry of Natural Resources and Forestry. OMNRF_processors_2020.csv: This data constitutes the estimated total number taxidermists and cervid meat processors by county in Ontario, Canada for hunting season 2019-20, as recorded by the Ontario Ministry of Natural Resources and Forestry. OMNRF_cervid_facilities_2020.csv: This data constitutes the estimated total number of captive cervid facilities by county in Ontario, Canada for the year 2020, as recorded by the Ontario Ministry of Natural Resources and Forestry.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".