Laboratory 3 - Dataset 2: Using Pan Traps to Estimate Insects Abundances
Bibliographic record
Abstract
Meta Data:Habitat - The datasets were collected in grassland and woodlot.Pan Trap Colour - The colour of pan traps used for this dataset were blue and yellow.Number of Insects - The insects captured in the pan trap were counted individually. Methods: To record this dataset, 10 yellow pan traps were placed randomly in grassland and 10 blue pan traps were placed randomly in woodlot. Each trap was filled half way with soapy water. The same colour of pan traps were used in each habitat because this way it would not get mixed up with another group's traps. After one hour, the total number of insects captured in each trap were individually counted and recorded. Study Site: The dataset was collected on September 29th, 2016 between 3:10 pm- 4:10 pm at Danby grassland and woodlot at York University Keele Campus, Toronto, Ontario, Canada. The weather was cold with a temperature of 15°C with slight wind and drizzle. Equipment: Yellow and blue pan traps and soapy water Hypothesis: There are more insects in grassland compared to woodlot because there are more resources to vegetation compared to woodlot. Predictions: 1) More insects will be captured in grassland compared to woodlot. 2) Different species of insects can be captured in grassland. 3) Less insects will be captured in woodlot because it is closed off and has less resources to vegetation. Group Members: Abesan Balakumar, Matthew Chiang, Andrew Nguyen
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".