Cross-biome scale data of summer bird assemblages across various habitat types in Alberta, western Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alberta covers diverse types of ecosystems including boreal forests, Rocky Mountain subalpine forests, as well as temperate grasslands in western Canada. The location of Alberta at the convergence of the Pacific and Central Flyways highlights its importance for bird conservation. However, recent climate change is altering vegetation, reducing wetlands, which can influence habitat niche availability of birds. While there are certain efforts of bird monitoring, public data are often limited to checklists or species presence, community-level datasets of birds collected consistently across biomes is rarely available. Here I conducted large-scale 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). These surveys span woodlands, wetlands, grasslands, farmlands, and urban areas, providing a comprehensive bird species inventory. This preliminary dataset establishes a baseline for understanding avian biodiversity across Alberta and supports future research and conservation strategies aimed at mitigating climate-induced habitat changes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it