Insect Data from Grassland and Woodland Quadrat Samples
Bibliographic record
Abstract
Data was collected using ten quadrat samples taken in both a grassland and woodland area in Toronto Ontario. The quadrants were used to observe species identity, number of insects in each species, total number of species, and the total number of insects. The data included insects in the quadrat ground area and insects flying within a meter above the quadrat. This data was collected by P Chow, N Gajendran, C Lau, and T Sequeira who all had designated roles in the data collection process. N Gajendran took five steps in any direction in attempt to keep quadrant sampling relatively random. N Gajendran identified a species while P Chow counted the number of insects in the quadrat of that species. T Sequeira identified flying species flying within a meter above the quadrat while C Lau counted the number of insects of that species. Data was recordedand number of insects of each species were added to record total number of insects within quadrat area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.001 |
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 teacher head, 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".