Cambial mortality in declining Austrocedrus chilensis forests: implications for stand dynamics studies
Bibliographic record
Abstract
Living trees in declining Austrocedrus chilensis (D.Don) Pic. Sern. et Bizarri forests exhibit cambial mortality or death of part of the cambium causing incomplete formation of rings around the circumference, which represents a potential source of error when determining tree ages and years of death. We sampled 12 stands to quantify the incidence of cambial mortality and tested whether its occurrence is independent of the health condition and canopy position of living trees. Trees were cored and statistically cross-dated. Cambial mortality was identified when the outer-ring date differed from the year of sampling. Of the 811 trees sampled, 307 exhibited cambial mortality with duration ranging from 1 to 39 years. Cambial mortality was most common in subcanopy trees independent of their health condition. Symptomatic canopy trees also exhibited a greater than expected incidence of cambial mortality. The presence of cambial mortality represented a source of error when ages were estimated from ring counts, reiterating the importance of cross-dating for determining accurate tree ages. Cambial mortality also poses complex challenges for estimating the years of tree death. Using the cumulative frequency of cambial mortality duration, we present a novel method for estimating error in tree ages from ring counts and selecting class widths to more accurately depict age structures and mortality rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".