Use of thin sections to improve age estimates of Nothofagus pumilio seedlings
Bibliographic record
Abstract
This paper reports the use of thin sections to improve precision and accuracy when ageing seedlings of Nothofagus pumilio. We compare 2 methods for determining the number of rings on 85 basal disks from seedlings: (1) ring counts of wood disks viewed with reflected light and (2) ring counts of thin sections viewed with transmitted light. Samples included 14 to 53 rings. Comparison of ring counts from the 2 methods revealed discrepancies of 1 to 12 y for 85% of the seedlings. Two sources of error were identified. In 70 of 85 samples, up to 12 incomplete rings explained differences between ring counts of 2 radii on the same wood disk. Secondly, the small radii, large number of rings, and diffuse porous nature of the wood resulted in frequent errors when visually detecting rings on wood disks. One to 3 false rings were detected in 15 seedlings. Narrow and suppressed rings in 57 samples resulted in under-estimates of 1 to 12 y for ring counts on wood disks relative to thin sections. High variation in the incidence of narrow rings limited our attempt to visually crossdate ring-width patterns among seedlings. However, counts of thin sections allowed us to identify false and incomplete rings, improving precision and accuracy when estimating seedling age. For studies of slow-growing seedlings that require high-resolution age estimates, we recommend the preparation and dating of thin sections as outlined in this paper.Nomenclature: Muñoz-Schick, 1980.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".