Sweep-nets and Transects in Danby Grasslands Estimating Insect Abundance
Bibliographic record
Abstract
The following is a dataset observing the insect population in the Danby Grasslands at York University Keele Campus in Toronto Ontario. The collection of data took place on September the 29th from approximately 2:40pm- 3:15pm. It was approximately 18 ⁰C with constant precipitation in the form of rain, the wind speed was not available at the time of collection but it was insignificant. There was overcast with little natural sunlight appearing through the clouds. The collection of data was carried out by myself along with the other members of my laboratory group from BIOL 2050 Katie, Katherine, Ashley and Ava. A total of 10 trials were to be conducted using transects and sweeping-nets. Each trial consisted of a sweep-net being moved back and forth in a “sweeping” motion along a transect of 15 metres for the approximate time of 2 minutes and 30 seconds. Each trial used a new random location to place its transect and begin sweeping. Before the beginning of each trial the sweeping-net was removed of all debris including insects prior to sweep. Each transect was swept from start to end a total of 15 metres (ie. not back and forth on a transect of 7.5 metres for a total of 15 metres covered). After each sweep the insects collected were recorded and removed from the sweeping-net. Unfortunately most likely due to the weather there was a lack of variation in the insects found or total unique RTUS, with only Field crickets and Garden spiders being found. This was also evident by the number of trials that had zero insect captures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".