Bibliographic record
Abstract
This dataset was collected in the grassland east of Stong Pond (43°46'15.5"N 79°30'26.2"W) at York University,4700 Keele Street,Toronto, ON M3J 1P3, in Canada. The date was September 23rd,2014, from 4:00 pm to 4:30 pm. The weather was warm but cloudy, and the temperature was between 20 and 25 degrees Celsius. The area where the sampling took place,at the moment of it,was characterized by an abundance of the following species of plants : Canada Goldenrob (Solidago canadensis), White Aster (Aster ericoides),Queen Anne's Lace (Daucus carota),Common Dandelion (Taraxacum officinale),Graminoids ( grasses). To record this dataset,quadrats were placed randomly in the grassland,considering that it was uniform in terms of scale of environmental heterogeneity. The quadrat used was a square frame tool made of metal,so it bounded the area of a square meter (1m x 1m). Simple random sampling was the method adopted : the first quadrat (n=1) was placed in a random location within the designated area of the grassland and this process was replicated until n=25 plots were sampled. The total abundance of plants,the total number of different plant species,the total cover of all vegetation and the total cover of grasses were estimated for each sample within the area of the quadrat. The abundance of plants was obtained doing a quick mental count of every individual whose roots were inside the plot ( plants located at the edge of the quadrat were not counted if the stems were in and the roots were visually identified outside ) . The total number of different plant species was definited using an identification key based on morphology. The total cover of all vegetation and the total cover of grasses were valued looking down on the quadrat from above and estimating the observed percentage of the area,in a square meter,occupied by vegetation first and then by grasses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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".