Does coring contribute to tree mortality?
Bibliographic record
Abstract
We assess the potential of increment coring, a common method for measuring tree ages and growth, to contribute to mortality. We used up to 21 years of annual censuses from two cored and two uncored permanent plots in the Sierra Nevada of California, to detect changes in mortality rates 12 years following coring for individuals >5 cm DBH from two coniferous species, Abies concolor (Gordon & Glend.) Lindl. (white fir) and Abies magnifica A. Murr. (red fir). Using a randomized before-after control impact (BACI) design, we found no differences in mortality rates following coring for 825 cored and 525 uncored A. concolor and 104 cored and 66 uncored A. magnifica. These results support the view that collecting tree cores can be considered nondestructive sampling, but we emphasize that our 12-year postcoring records are short compared with the maximum life-span of these trees and that other species in different environments may prove to be more sensitive to coring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".