Comparison of methods to quantify respirational carbon loss of coarse woody debris
Bibliographic record
Abstract
Carbon (C) loss from coarse woody debris (CWD) may be important in forest ecosystem C budgets, yet there are no standard methods of quantifying it. Here we assessed respirational C loss of log segments of European beech ( Fagus sylvatica L.) and Norway spruce ( Picea abies (L.) Karst.), to compare the two main measurement approaches (the static and dynamic methods using soda lime and an infrared gas analyser, IRGA) with three different measurement options for CWD logs. These included (i) incubation of the whole log segment, (ii) the use of small in situ chambers on the curved log surface, and (iii) extracted wedges of wood. On average, significantly higher amounts of CO2were measured with the IRGA (125%) compared with soda lime. In addition, the soda lime method requires careful calibration of incubation length and amounts of soda lime used. Regardless of the measurement method and tree species, substantially higher amounts of CO2were measured for whole log segments than for the other two options. Measuring respiration with small in situ chambers on logs or extracted wedges might underestimate real CO2flux by up to 74%. We therefore recommend measurement of CWD respiration using gas analysers for large log segments.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".