Additional file 1 of Understanding the dog population in the Republic of Ireland: insight from existing data sources?
Bibliographic record
Abstract
Additional file 1: Table S1. The number of dog licences issued in Ireland during 2000-2020, by type of licence. These data were collated by the Department of Rural and Community Development and are available at https://www.gov.ie/en/collection/879d4c-dog-control-statistics/ . Table S2. The number of dog microchips issued in Ireland by Animark, Fido and Microdog ID from 2015 to 2020 and by the Irish Kennel Club from 2015 to 2020. Table S3. Annual statistics relevant to dog control centres in Ireland during 2004-2020, including the number of dogs on hand at the start and end of each year, the number of incoming dogs (either surrendered/collected or seized), the number of dogs, and the number of outgoing dogs (euthanised or died from natural causes, reclaimed/rehomed or transferred to a dog welfare organisation). These data were collated by the Department of Rural and Community Development and are available at https://www.gov.ie/en/collection/879d4c-dog-control-statistics/ . Table S4. The number of dog movements from Ireland to third countries during 2016-20, as recorded in TRACES, which is the online platform of the European Commission to facilitate sanitary and phytosanitary certification of animals, animal products, food and feed and plants, into the EU, for intra-EU trade and EU exports ( https://ec.europa.eu/food/animals/traces_en ). Table S5. The number of dogs recorded on commercial flights into Dublin airport during 2015 to June 2021. Table S6. The number of dogs recorded on commercial flights into Shannon airport during 2015 to June 2021. Table S7. The number of dogs recorded on commercial ferries into Cork Roscoff from July to October 2020. Table S8. The number of dogs recorded on commercial ferries into Cork Ringaskiddy from January to February 2020. Table S9. The number of dogs recorded on commercial ferries into Rosslare, Co. Wexford from 2018 to May 2021.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.781 | 0.152 |
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".