Bibliographic record
Abstract
The purpose of this experiment was to determine whether the height of Canada Goldenrod plants is correlated with their density. This dataset was gathered in the same location as the pilot lab. The Lumsden Ave. entrance to Taylor Creek Park has the most abundant population of goldenrods, compared to other entrances. This time the lab was conducted further into the shrubbery in order to get higher densities of goldenrods in the quadrats. Another trial was run closer to the trail to get lower densities of goldenrods. This was done to control the densities and determine the relationship between height and density. At each site fifteen 1x1 metre quadrats were marked off in a linear direction. The densities were measured by counting the number of individual plants in each quadrat. 0-3 plants were considered low density quadrats, 4-6 plants were considered medium density quadrats, 7+ quadrats were high density quadrats. The average height of plants was determined by averaging the heights of all plants in the quadrat. Both sites were visited on the same day. There were 2 main sites (N=2) with a total of 30 quadrats (n=2) distributed between them. The density and height data were recorded separately in order to ensure clarity. They will however be graphed together later when displaying visuals for the relationship between density and plant height.
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) | 0.034 | 0.040 |
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".