Bibliographic record
Abstract
Description/Method: The data for “Grassland Dataset 1” was collected on Friday September 26 2014 from 15:00-15:45, in a grassland area at the Keele Campus of York University in Toronto, Ontario. Weather conditions included clear, sunny skies with no prevalent cloud cover, little to no wind and a temperature of 25 degrees Celsius. A 2x2m quadrat was placed in a random location in the grassland area by the researcher. Variables to be collected in this dataset included: abundance of plants, number of individual species, vegetation cover (%) and grass cover (%). Each quadrat was visually divided into quarters by the researcher in order to count the abundance of plants (the total number of individuals) and multiplied by 4 to obtain the abundance of plants in the entire quadrat area. Individuals of grass species were not taken into account to avoid an increased likelihood in miscounting errors. All remaining data was collected by observation and estimation. The quadrat was relocated to 24 subsequent unique random locations in the grassland area, in order to obtain a total of 25 samples of the experiment. The abundance of plants and number of unique species were expected have relatively constant numbers respectively, due to the high density of vegetation and grass in the grassland area. Researchers also expected the abundance of plants and the vegetation cover to have a direct relationship. Fellow York University biology student, Kristina Gulli was my research partner in the conduction of this experiment. Fellow group members also included: Yu Xiao and Naz Naji.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.067 |
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".