BIOL2050 Lab 2, Dataset 1: Measuring the Abundance of Different Plant Species in York University Grassland Using Quadrats
Bibliographic record
Abstract
Variables "Total # of Plants"- A counted estimate as seen by the naked eye of the total abundance of plants within a quadrat. "Total # of Different Plant Species"- Counting the number of different plant species found within a quadrat after careful examination by the naked eye. "Total Cover of All Vegetation (%)"- Estimating the percentage of vegetation found within a quadrat (opposed to sparse areas of bare soil) as seen by the naked eye. "Total Cover of Grasses (%)"- Estimating the percentage of grass found within the quadrat (out of the percentage of vegetation already present), again using the naked eye. MethodThe dataset was collected on September 21, 2016 in a grassland area at York University (Keele Campus, Toronto). A square quadrat was used to measure the abundance and variety of plant species present within the grassland. After numbering the 4 sides of the quadrat, a random number generator was used to ensure random placement 25 times within the grassland. All measured variables were examined and estimated using the naked eye.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.029 |
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".