Lab 2: Techniques & data: field training with plants (traits, transects, and quadrats) DATASET 1
Bibliographic record
Abstract
To measure plant diversity in locations around campus, n = 25 quadrats (1m x 1m) were randomly placed in the grasslands around Stong Pond at York University (43°46'13.2"N 79°30'25.9"W) on September 23rd 2014 from approximately 4:00pm to 4:30pm. The weather was party cloudy with a temperature between 20 and 25 degrees Celsius. To begin the sampling procedure, the first quadrat was randomly placed in an area along the grassland boundary. Subsequent quadrats were randomly sampled by walking 5-10 steps in a random direction from the last quadrat placed until another boundary of the grassland was reached. Once a boundary was reached a new sample area in the grassland was randomly chosen by walking 30m from the previous sample area and the process was repeated until n=25 plots were sampled. The total number of plants along with the number of species, vegetation coverage and grass coverage were estimated for each sample. Number of plants were counted by observing the amount of unique stems in the sample. Species were differentiated based on morphological characterstics using an identification key. Coverage was estimated using the observed percentages of the total sample area occupied by the plants of interest and grass, respectively. At the time of sampling, the most abundant species included the following: Common Milkweed (Asclepias syriaca), Canadian Goldenrod (Solidago canadensis) and Late Purple Aster (Aster prenanthoides). Note: Definitions of variables are located in column C of the Meta sheet
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.066 |
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".