Bibliographic record
Abstract
A dataset observing the bird population in the Danby Woods and Grassland at York University in Toronto Ontario. The project was completed by myself, Adamo, Katherine, Ava, and Ashley- a group of ecology students. The project ran from approximately 3:50 to 4:25 p.m. on September 29th. The experiment took place under dark cloudy skies and in roughly 18 ⁰C weather. The conditions of weather were constant precipitation and a continuous wind. The experiment consisted of observing the number of birds, and the number of different Recognizable Taxonomic Units (RTU) in a given area. The area was determined using a belt transect, which was set up by placing a 9 metre transect along the ground and watching for any birds within an 8 metre radius of that transect. When a bird was spotted, one member of the group estimated the distance with respect to the horizontal axis of the ground. The group had decided to not take height of the bird from the ground into account. The transect was observed for 3 minutes to ensure that the mobility of birds was taken into account in the estimation of the population. For each bird sighting, a rating from the Beaufort scale of wind speed was decided as well. The experiment was replicated 5 times in the grassland, and another 5 times in the woods with a transect in a different spot each time. Most likely due to the rain, there was a minimal bird count with zero sightings in the woods and only a total of 5 birds spotted in the grasslands. There also was not an extensive number of RTU’s. Instead only sparrows, robins, and mourning doves were seen from the transects.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 0.007 |
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".