Examining the Diameter/Condition of Trees from the Edge of a Woodlot to the Center.
Bibliographic record
Abstract
Methods: The data was collected by using a transect measuring tape to make a straight line from the edge of the forrest to the center. When an adult tree was noticed along this line the distance from the edge was recorded as well as the diameter and condition of the tree. Hypothesis: I predict that as you go deeper into the woodlot the diameter of the trees will increase and the condition will lead to more larger trees, because they will have more room to grow and be in a less disturbed area. Study Site: Danby Woods, York University, Toronto, Ontario, Canada. Predictions:1) Trees deeper in the woodlot will be a lot larger.2)There will be more total trees deeper in the woodlot.(they will be closer together)3.There will be more huge green canopy trees near the center of the woodlot. Metadata:Distance.Between.Consecutive.Trees: numerical- A research lab was conducted to measure the distance between consecutive trees throughout the woodlot using a transect measuring tape, in Danby Woods.Diameter.Of.Tree.At.Breast.Length: numerical- A research lab was conducted measuring the diameters of trees at breast length from the edge of a woodlot to the center, in Danby Woods.Condition.of.Tree: categorical- A visual survey was done to determine the condition of trees. Condition was identified as dead, living or having a huge green canopy, in Danby Woods. Group Members:Sarah PecileNiusha TaatiLaura NatiRija Ghani
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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 | 0.001 | 0.001 |
| 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) | 0.005 | 0.002 |
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".