Lab 3: Techniques & data: field sample training with animals (pans, pitfalls, sweeps, and distances) - Distance Based Dataset
Bibliographic record
Abstract
To measure bird diversity in different habitats on campus, the frequencies of various bird species in Saywell Woodlot(43.770222, -79.508942) and in the grasslands around Stong Pond (43°46'13.2"N 79°30'25.9"W) were measured on September 30th, 2014 from 3-5pm. At the time of measurement, the weather was cloudy with a temperature around 20 degrees Celsius. To measure the frequency of bird species, n=10 transects were performed (approximately 25 meters in length) with n=5 performed in the grassland habitat (denoted as G) and n=5 performed in the woodland habitat (denoted as W). The different types of species of birds as well as their frequency were recorded as we proceeded along the transect. We walked along the length of the transect in both directions until birds were spotted, after which a new transect was performed. The transects were performed approximately 10 meters apart in different directions including random areas at the boundary of each habitat. Species were differentiated using a bird identification handbook (differentiated based on morphology). The distance from the transect to the group of birds was approximated (i.e. not directly measured) using binoculars. Distances from the transect which appeared to be greater than 500 meters were denoted as “500+” (500m was the cutoff point). The frequency of the birds that could not be identified at the distance they were observed at were recorded under the “Unidentified” column. Wind speed at the time of sampling was consistently 0 km/h. Species observed included B. Canadensis (Canadian Goose), L. Argentantus (Gull), P. Domesticus (House Sparrow) and T. Migratorius (American Robin). Note: Variable definitions are in column C of Meta sheet.
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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.068 |
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".