Habitat Suitability in the Eyes of the Beholder train and test datasets (70/30 split).
Bibliographic record
Abstract
This dataset combines modified data from the OBBA (2001-2005) (Bird Studies Canada, 2008), the Avonet dataset (Tobias et al., 2022), and the Ontario Land Cover Compilation v2 (Ontario Ministry of Natural Resources and Forestry, 2014), specifically covering the Canadian portion of the Great Lakes Basin Watershed. It includes data on 211 species from the OBBA, though all location-identifying information has been excluded. The full dataset can be requested via the Nature Counts Portal (https://naturecounts.ca/nc/default/explore.jsp#download). Species trait data originate from the Avonet dataset, with detailed trait descriptions available from the original Avonet dataset. Land cover classifications were simplified into two categories: "Natural" and "Human Modified." Although an "Other" category was initially present, it was removed prior to model development. The data was resampled into various pixel sizes, ranging from the original 15m resolution to 150m, 300m, 500m, and 1000m. The dataset is divided into a 70/30 train-test split and was used in building random forest models. For further details, please refer to the original dataset sources. References Bird Studies Canada, Environment Canada’s Canadian Wildlife Service, Ontario Nature, 553 Ontario Field Ornithologists and Ontario Ministry of Natural Resources. (2008). Ontario 554 Breeding Bird Atlas Database. https://naturecounts.ca/nc/default/explore.jsp#download Ontario Ministry of Natural Resources and Forestry. (2014). Ontario Land Cover Compilation 705 Data Specifications Version 2.0. https://ws.gisetl.lrc.gov.on.ca/fmedatadownload/Packages/OntarioLandCoverComp-v2.zip Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Sayol, F., Neate‐Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño‐Centellas, F. A., Claramunt, S., Darski, B., Freeman, B. G., Bregman, T. P., … Coulson, T. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.095 |
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".