Bibliographic record
Abstract
Group Members: Kamil Adamczewski, Joelle Brooker, Brittney Jorisch, Karin Yosefi. This dataset was collected at a grassland near Danby Woodlot near the campus of York University in Toronto. It was collected on September 24, 2015 from 3:00pm to 3:15pm. The weather was sunny, partly cloudy, with a temperature of roughly 24 degrees Celcius. The grassland was fairly flat, had a patchy distribution of flowers/plants and had its grass cut over a month ago. The experiment consisted of throwing a 1 metre by 1 metre quadrat 20 times (n=20) onto the grassland. It was thrown randomly by group members after they had walked an unpredetermined amount of steps. After the quadrat had landed, the data for total abundance, % cover, % grass cover, and richness was collected. The purpose for collecting this data was to practice new sampling techniques that could be used to find correlations between variables being studied. For this dataset, the purpose was to find if there was a correlation between the total abundance of plants present in the quadrat to the cover size and number of species.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.067 |
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".