Bibliographic record
Abstract
The purpose of the experiment was to determine whether a corellation existed between the amount of sunlight an environment recieves and the amount of vegetation it holds. The location of data collection occured on the Danby woodlot and grassland areas of York University, Toronto, Canada. Along with four other group members, data was collected between 3-5pm on two separate days: October 19th 2015 and October 26th 2015. The weather on October 19th was 14 degrees. partially cloudy and windy and on October 16th it was 10 degrees, mainly sunny and no significant wind existed. The amount of vegetation and sunlight in both environments (woodlot and grassland) was determined as a percentage. Sample locations were determined by a random number generator: beginning from the middle of each environment, two numbers were generated; the first (1-4) determined the direction (N, E, S, W) and the second (1-100) determined how many steps were to be taken in that location. Vegetation percentage was determined by using a 1 by 1 metre quadrat, only living vegetation that had it's roots in ground were counted. Sunlight percentage was also determined by using a 1 by 1 metre piece of blank cardboard which was laid just on top of the quadrat; the amount of sunlight that reflected off the cardboard was determined as a percentage. 50 samples were taken in each environment, each day: totalling to 100 samples for each environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".