Bibliographic record
Abstract
Methods This dataset was collected on September 29, 2016 at approximately 4pm, in the Dandy grasslands and woodlot near York University. The weather for the day of sampling was 15°C with light rain (Rain: 1-3 mm Wind: 20 km/h E Wind gust: 30 km/h Humidity: 82). In each habitat type, five different 10 m transects were placed in randomly chosen spots. The number of bird sightings in a one minute interval was recorded for each transect. Wind intensity for each transect was estimated on a scale from 1 to 5, 5 being the most intense. Wind speed and weather information for the general area was obtained from the website https://www.theweathernetwork.com/ca/weather/ontario/north-york at the time of sampling. The number of different bird species and the distance from the transect was also estimated for each sighting. Hypothesis Wind intensity has an impact on the number of birds that are active. Predictions 1) There will not be many birds active due to the high wind intensity. 2) There will be a low diversity of bird species, because smaller birds may have difficulty navigating in strong winds. 3) There will be a higher abundance of flying birds in the open area (grasslands) than in the woodlands at the time of sampling, due to the fact that most birds are diurnal.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.087 |
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".