Data of Several Species of Trees Collected in York University'sWoodlot
Bibliographic record
Abstract
The data obtained was from the woodlots of York University in Toronto. It was a forested area on the outskirts of the university and there was a lot of green canopy. There were different species of trees were present for observation and data recording. The data was collected in pairs, one person recording the numbers and measurements, and the other using the tools in order to obtain the measurements. The tools used were transect measuring tapes and normal measuring tapes. The data was collected from 10 arbitrary trees from the edge to the centre of the woodlot. Walking in a straight line, every tree encountered that was twice the data recorder’s height was recorded. The dichotomous tree guide was used to compare and match the tree leaves according to its species. Data from the species of Sugar Maple, Red Oak, Black Cherry, Bass Wood, Red Pine and White Spruce were collected. The following variables recorded were the species of the tree, the diameter of the trunk, the distance from the previous tree and the condition of the tree itself, using numbers 0,1,and 2. Each tree was labelled one to ten in order to chronologically keep track of the data and recordings. The species of the tree (Sugar Maple, Red Oak, Black Cherry, Bass Wood, Red Pine and White Spruce) was obtained by matching the leaves to the dichotomous tree guide provided in the lab manual. The diameter was the measurement of the tree trunk at breast height, measured in centimetres using the normal measuring tape. The distance was recorded in feet and stated the length from the previous tree to the current one being recorded using the transect measuring tape. The condition of the tree was measured using numbers, 0 representing dead, 1 representing living & small canopy, and 2 representing living & large canopy.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".