Bibliographic record
Abstract
The Pan Trap dataset was collected on the 1st of October 2014 at approximately 4:50pm, in both the grassland and woodlot area at the Keele campus of York University in Toronto, Ontario. It was a wet day as it had rained the night before and towards the end of the lab it was cloudy with showers. At the beginning of the lab, nine bowls of different colours filled with soapy water were placed at intervals in a random site at the grassland area. The soapy water was added to each bowl in order to kill whatever insects entered into it. The bowls were placed in a sequence of light blue, yellow and white so as to observe the insects that were attracted to each colour. Three blue coloured soapy bowls were also placed at a random site in the woodlot area. They were all the same colour as there were no more bowls of the other colours. Also the bowls were placed in a location where there would be minimal disturbance by other members of the lab so as not to scare away the insects. At the end of the lab, the number of insects and the number of different recognizable taxonomic units (RTUs) were determined in both the grassland and the woodlot. In the grassland area, a Large Carpenter Bee (Xylocopoa virginica)-order Hymenoptera, was attracted to one of the yellow bowls, then in one of the blue bowls there was a three-spotted jumping spider (Phidippus audax)-order Araneae. In another blue bowl in the grassland area there was Grasshopper- order Orthoptera and no insects were attracted to the white bowls. In the woodlot, only one insect was observed in all three blue bowls and they are called Aphids- order Hemiptera. A group of four students performed this experiment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.039 | 0.028 |
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".