Bibliographic record
Abstract
The experiment conducted on the 20th of September 2016 at York University in the grasslands was to examine if their was any relationship between vegetation percentage and the amount of sunshine present in a 1 meter by 1 meter area unit. Method: Quadrats were thrown at random 25 times in the grasslands of York University. Once thrown, the number of plant species, individuals of all the plant species, vegetation percentages and grass percentages were taken. Hypothesis: There is a correlation between vegetation percentage to the amount of sunshine present. Prediction: There is a positive correlation between sunshine and vegetation percentage. Quadrats that were randomly thrown at places with more shade had less vegetation percentage and more grass percentage while quadrats thrown at places with more sunshine had more vegetation percentage and lass grass percentage. This prediction was made due to the fact that plants need sunlight to go through photosynthesis and produce its own food to survive. Meta data: -The quadrat thrown was a 1 meter by 1 meter quadrat -The number of plant species in this quadrat were recorded -Plant ratios to grass ratios were recorded -Total number of individuals of all plant species were recorded in the 1 meter by 1 meter quadrat -Plant species included Canadian Goldenrod, Heath Ester,Butter and Eggs,Common Thistle and Late Purple Aster -There were a few small tree species but since the scientific name wasn’t known tree was just used -The data was then put on an excell sheet and uploaded on figshare.com
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.082 | 0.056 |
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".