A Survey of the Vegetative Abundance, Diversity and Cover in Grasslands using Quadrat Measurements
Bibliographic record
Abstract
Methods: The total density of various plant species, abundance and coverage was measured in a square meter quadrat for a total of 25 repetitions. The vegetation in 1/16 of the quadrat was used as an approximation of the density of the entire quadrat. Study Site: The study took place in a grassland plot outside of York University Keele Campus, located in Toronto. The weather at the time of the survey was overcast, with slight rainfall.The measurements were taken using a quadrat. Measurements were taken from varying sectors of the grassland in order to ensure observations were representative. Hypothesis: Grasslands can sustain a high density of vegetation of a variety of plant species due to the characteristic lack of presence of large shrubbery or trees. Predictions:1) As the total grass cover in a quadrat increases, the number of different plant species observed with decrease.2) The increased presence of large plant species will decrease the overall vegetation cover in a quadrat.3) As the total abundance of observed plants in a quadrat increases, so will the total grass cover increase. Attributes 1)Total Abundance of Plants: Numerical2)Total Number of Different Plant Species: Numerical3)Total Cover of All Vegetation Within Plot: Numerical4)Total Cover of Grasses: Numerical
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".