Sources of variability in tissue chemistry in northern hardwood species
Bibliographic record
Abstract
Measurements of tree tissue chemistry are influenced by the precision and accuracy of laboratory analyses, sampling position within the tree, variation among replicate trees of the same species, and variation from year to year. We characterized these sources of uncertainty for six northern hardwood species and compared them with observed rates of long-term change. Uncertainty associated with laboratory quality control was small (1%–5%) and differed among elements, with K concentrations exhibiting the lowest accuracy and precision. Sampling position within the tree was more important for branches (the coefficient of variation was 23%) and wood (37%) than for foliage or bark (12% for both) (p < 0.001). Foliar N and P concentrations in leaves were less variable than other elements or tissue types both from tree to tree (p = 0.02) and from year to year (p = 0.03), which means that more samples would be needed to detect differences over space or time for Ca, Mg, or K in branches or wood. Concentrations of foliar N increased over 25 years at the Huntington Forest (p ≤ 0.03) by > 16%. Uncertainty analysis can be used to guide the allocation of sampling effort, depending on the elements and tissue types of interest and the objectives of the study.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".