Striking a balance: Safe sampling of partial stem cross-sections in British Columbia
Bibliographic record
Abstract
Dating cambial injury on a tree is an important objective of ecological research that determines the timing of disturbances such as fire, insect outbreaks, tree falls, or human modification of trees. To accurately date cambial scars requires either a full or partial cross-section of the wounded area so that the scar morphology can be observed, the scar tip(s) identified, and ring widths cross-dated to assign an exact year and (or) season to each scar. Partial cross-sections are less destructive; however, they are rarely used in British Columbia due to potential violations of existing standard-of-care procedures regarding wildlife/danger trees. We outline new safety criteria, sampling procedures, and documentation required to safely extract partial cross-sections. Based on British Columbia's Wildlife/Danger Tree Assessment methods, the three safety criteria are: (1) area removed should not exceed 25% of total cross-sectional area, (2) circumference removed should not exceed 25% of total circumference, and (3) shell thickness remaining after sampling is greater than 30% of the radius of the tree. Documenting the location of partially sectioned trees is critical, as it allows management agencies to inform future forest users of the location and condition of the modified trees.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".