Distance Based Sampling of Birds in Grassland and Woodlot Habitats in Danby Woods at York University
Bibliographic record
Abstract
The dataset was collected on Monday September 28th, 2015 at Danby Woods and Grassland at York University in Toronto, Ontario between 3:00pm- 4:30pm. The weather conditions of the location was about 20 °C cloudy with a slight wind gust. Although it was not raining during the experiment, the grassland and woodlot area was a little damp because it rained prior to the experiment. The experiment was done in groups of 4 which included Aziza, Sahar, Paula and Shahana. However, this experiment was conducted with the help of Sahar. The purpose of this experiment was to observe bird distribution and abundance in woodlots vs grassland areas, and to learn techniques of collecting distance based dataset using a transect. This experiment consisted of recording the frequency of birds, species/family, the wind speeds and the distance between the birds and the transect. The experiment was replicated 5 times in woodlot and 5 times in grassland. Each transect was observed for 3 minutes to take the mobility of the bird into account. A 15m transect was placed randomly on the ground to record the distance between the birds and the transect. The distance ranged from 0-15m, and was approximated along both sides of the horizontal axis of the transect. Also, for this dataset height was not taken into consideration. When a bird was spotted one member of the group would record the estimated distance and the frequency of the birds. The other individual would try to identify the bird using a bird identification key that was given. The wind speed was decided by using the Beaufort scale that ranged from 0(calm) to 12(hurricane), and a number was assigned by the way the wind affected surrounding things like plants and trees. There was not a huge abundance of bird species most of the ones that were spotted were Herring Gulls, Northern Mockingbird, Sparrow and Black Phoebe. Rain could have been a factor that limited the abundance of birds in the woodlot and grassland areas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".