Dataset for: "Cross-biome scale data of summer bird assemblages across various habitat types in Alberta, western Canada" (Hisano, M)
Bibliographic record
Abstract
The summer surveys of breeding birds across Alberta’s six major ecoregions (Northern Rockies conifer forests, Alberta-British Columbia foothills forests, Mid-Canada Boreal Plains forests, Canadian Aspen forests and parklands, Montana Valley and Foothill grasslands, and Northern Shortgrass prairie) in Canada. These surveys span woodlands, wetlands, grasslands, farmlands, and urban areas, providing a comprehensive bird species inventory, although preliminary. The dataset includes the number of individual for each avian species obtained by the point-count method (75 m radius, 10 minutes; July-August 2024).Some definitionsElev: elevation (m)Forest_type: DEC = deciduous broadleaves (e.g., Populus, Betula), ESC = early-successional conifers (Pinus), LSC = late-successional conifers (e.g., Abies, Picea, Tsuga)Date_ymd: Survey dateStart_time: Time started the point-count survey (for 10 minuets long)Counts: Individual counts of each species at each study siteEcoregions are based on Dinerstein et al. BioScience 67: 534-545.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 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) | 0.072 | 0.036 |
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".