Birdwatching Deep Dive Dataset Observing Species Interaction and Behaviour
Bibliographic record
Abstract
The data collected from the birdwatching pilot examines additional behaviour of how species interact with each other in regards to aggression and territoriality. The dataset is presented in a table which contains 6 columns containing information on how data was compiled. The first column contains the repetitions which are the dates the experiment, birdwatching, took place. The second column kept track of the researcher who was conducting observation. Column three was the area in which data was being collected which was at a local creek by Elmcreek park in the city of Mississauga Ontario over the span of two days for a total of two and a half hours divided across both days. Column four was the common-name species observed and column five compiled the frequency in which that species participated in a particular behaviour not as an individual but as separate individuals. Column six was the behaviour the birds were observed doing from foraging to species interactions. Data was collected in the early morning between 10 am-12 pm on September 12th and 2 pm-3pm on September 13th. The initial day of sampling the temperature was sunny with slight clouding whereas it was cloudier on the second day with a little more wind. Data was collected again over a period of two days two weeks later where the majority of species observed remained the same from the initial pilot, the data collected on September 26th was between the hours of 11 am-12pm whereas the data collected on September 27th was collected between the hours of 8:45am-11am.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.031 | 0.032 |
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".