Field Sample Training with Plants - Sampling tree populations & communities with transects and trait sampling techniques
Bibliographic record
Abstract
Methods: In order to sample the woodlot, a transect measuring tape was used to measure a straight line from the edge of the woodlot to the center. Every instance we encountered an adult tree that was twice our height, we recorded the distance from one tree to the next, the diameter at breast height (dbh) of each tree, and its condition (0=dead, 1=living, 2=huge green canopy). This data was recorded for 10 pairs of trees, which is 20 trees in total. Study Site:This study took place on September 22, 2016 in Danby Woods at York University Keele Campus, Ontario, Canada. The weather was very hot and sunny with a little bit of rain and approximately 25 °C. Transect measuring tapes were used as equipment and to make measurements with. Hypothesis: There is a correlation between the distance as you go into the center of the woodlot and the living condition of trees because in the center of the woodlot, there are less environmental disturbances such as wind, allowing those trees to live longer. Also, since there is less sunlight in the center, as other tree canopies act as shade, trees that already have grown prevent new trees from growing, so trees near the center of the woodlot are bigger and older and trees near the edge are smaller and younger. Predictions: 1) As you go deeper into the woodlot, there will be more trees that are huge green canopies because those trees are older and have been able to live long because of less disturbances compared to trees at the edge of the woodlot. 2) As you go deeper into the woodlot, trees will grow closer together and the distance between 2 trees will decrease because sunshine is limited and trees want to cluster to share the nutrients and sunlight. 3) As you go deeper into the woodlot, the diameter of tree at breast height will increase because the trees in the center are bigger and older. Metadata: Pair - Categorical - A pair of two trees were sampled and used to gather data. Distance apart (m) - Continuous - A transect measuring tape was used to measure the distance from one tree to the next tree encountered on the transect tape. Measurements were in meters. Dbh (cm) - Continuous - A measuring tape was used to measure the diameter at breast height of each tree encountered. Measurements were in centimeters. Condition - Categorical - The following scale was used to categorize the living condition of each tree encountered: 0=dead, 1=living, 2=huge green canopy. Condition was determined by visual observation. Group members: Krysten Zarivnij, Avani Abraham, Monica Matta
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".