Using dendrochronology to obtain annual data for modelling stand development: a supplement to permanent sample plots
Bibliographic record
Abstract
Permanent sample plots (PSPs), measured at 5- to 10-year intervals, are presently used to monitor stand development in boreal forests in Canada. These data cannot easily be used to study inter-annual variability in stand development processes nor can they monitor the effects of transient factors affecting stands annually because of their coarse temporal resolution. It also takes a considerable period of time to obtain time series of data for regions without PSPs. Long re-measurement intervals are necessary because instruments like diameter tapes, calipers and clinometers cannot discern annual growth in these slow-growing forests. Dendrochronological stand reconstruction techniques are a method that can be used to obtain annual data on forest growth and stand development. We show that these techniques can provide annual information on stand development patterns while periodic measurements of PSPs cannot, and that these data can be obtained in a short period of time, relative to waiting to obtain data from PSPs established today. Detailed, annual data will become more important in the future, as climate change will affect both forest growth and stand dynamics. Annual resolution data on these processes will be required to describe and account for these effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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 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".