Bibliographic record
Abstract
The data collection was situated in the Danby woodlot located at York University in Toronto. The conditions on the day of the data collection was overcast with temperatures around 19 degree celcius. The data was collected on Monday September 28th from 2:30pm to 5:30pm. The group members that assisted in the data collection were Antoni and Amina. We travelled to the grassland area of the woodlot to place down a transect of 30 meters. The grass area had larger plant and vegetation as well as long grass. The transect was placed using a random number generator to decide how many steps would be taken from a designated starting point until the transect was placed down. The transect was then walked for 15 meters by the researcher, as a group member timed the data collection for 90 seconds. A sweep net was used to catch as many insects as possible. The sweep net is used by sweeping the net close to the grassland floor back and forth quickly. After 90 seconds the amount of insects in the net was recorded and the number of different species was also recorded. The different species were vaguely identified in order to exhibit their differences. The insects were then released and the trial was repeated. For each trial half the transect was used therefore after every second trial the transect was randomly moved to a new location in the grassland. This collection was done to master the use of the sweeping net and to document the amount of insects that could be caught using it.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".