A Quantitative Analysis of Four Variables in grassland quadrats and woodlot transects
Bibliographic record
Abstract
Methods: For this lab, students worked in groups of 3-5 individuals. The number of members in this group was five. Four datasets were collected, one published by each group member. Transects were used within the grassland to collect this dataset: randomly place transect tape, walk along it, and select a visible plant species that is relatively simple to identify and recognize quickly. Every time a target plant species was recognized, the distance on the transect found, its height, the number of leaves, the number of flowers, and whether it was in a crowded patch of other plants (0 = open, 1 some plants nearby, 2 = quite a few plants, and 3 = very crowded bunch of plants within 50 cm) were all recorded. At least 50 individuals were sampled. If there was a need to run out another transect to capture 25 plants of the entire species, a random number table was used and the tape was moved over, in order to repeat the experiment. Outside Conditions: 90% shade cover, shards of sunlight coming through the spaces in the trees' canopy, slight breeze, little to no grass in the analyzed quadrats, dead maple leaves of varying colors, cool (lower than room temperature), last set of data recorded at 5:09 pm, occasional encounter of snails on plants, frequent observation of Purple Aster, and Canadian Goldenrod plants, maple leaf saplings varied in size from a bud, to a fully grown plant.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".