Bibliographic record
Abstract
For this animal field experiment, all data were collected in York University's Keele campus woodlot and grassland on Tuesday September 30, 2014 at around 2:30PM to 5:30PM in the afternoon. The weather forecast for that time of the day was cloudy with a high of 19 degrees Celsius and a chance of rain, although during the duration of the data collection precipitation was not observed in the area (according to http://www.accuweather.com/en/ca/toronto/m5g/september-weather/55488). The laboratory section was led by Taylor Noble. For the Woodlot dataset, it was collected by Jason, Coleen, Zainab, Donna and Leron. As for the Grassland data set, this was obtained from the other lab group with the permission of Taylor due to the time constraint. For the Woodlot dataset, my group utilized a belt transect method. Since it was very difficult to lay down the transect in a straight line in the woodlot, we estimated the length to be 50 foot paces which turned out to be around 25 metres from the transect. For every trial that we performed, we used the general straight line 50 foot paces for the distance-based observation. Anything that was visible in that straight line distance on either side of the line was accounted for in the data. We also used binoculars as well as a bird guidebook to help us better identify the species detected. As for the wind speed in the woodlot, we used the Beaufort scale and chose the seaman's terminology for the description (we also asked Taylor for confirmation on our estimation on the wind speed). Lastly, we used a combination of the transect and Pythagorean's theory to calculate the distance of the bird from the observer. Similar methodology was utilized in the grassland section of the dataset, but for a more detailed set of instructions please refer to the metadata of Taylor, D, Taylor P, et al.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".