A comparison of techniques for measuring density and concentrations of carbon and nitrogen in coarse woody debris at different stages of decay
Bibliographic record
Abstract
This research considered the relationship between the stage of decay and the concentration of nitrogen (N, %) and the ratio of carbon to N (C/N) in coarse woody debris. Density (g/cm3) was used as an indicator of the stage of decay. In samples collected from the red spruce Fraser fir (Picea rubens Sarg. Abies fraseri (Pursh) Poir.) forest of the southern Appalachians, density explained up to 60% of the variation in N and C/N in coarse woody debris. The technique used to estimate density was important. Laboratory-based methods (including displacement and mensuration density) explained the greatest degree of the variation, with coefficients of determination (r2) ranging from 0.39 to 0.59 (p < 0.001) for N and from 0.39 to 0.58 for C/N (p < 0.001). Field-based methods (including penetrometer and resisto graph readings) explained a smaller but still significant degree of the variation, with r2ranging from 0.17 to 0.25 (p < 0.01) for N and from 0.14 to 0.26 for C/N (p < 0.05). Consideration of within-bole heterogeneity in density improved the explanation of variation in N and C/N for a single bole. Density provides a continuous indicator of stage of decay that is not bound by the artificiality of discrete decay classification systems. Furthermore, statistical models relating density to N and C/N provide a means of hind casting and (or) forecasting changes in N and C/N in coarse woody debris at different stages of decay.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".