Characterization and decomposition of <i>Nothofagus pumilio</i> fine woody material
Bibliographic record
Abstract
Wood is one of the main reservoirs of carbon (C) and nutrients in Nothofagus pumilio (Poepp. & Endl.) Krasser forests; hence, its decomposition is essential for C and nutrient cycling. Chemical traits are one of the factors affecting wood decomposition, but there is little information describing these chemical characteristics. Our objective was to analyze the chemical traits of N. pumilio fine woody material (FWM) and their relationships with decomposition. We determined the contents of nitrogen (N), phosphorus (P), C, hemicellulose, cellulose, lignin, and extractives in two diameter categories (branches and twigs) and in two decay classes (DC1, sound wood; DC2, intermediate stage of decomposition). We also determined the decomposition rate constant (k) of each type of material. Contents of P, extractives, C, and hemicellulose in DC1 and C, cellulose, and lignin in DC2 showed differences between diameters. In twigs, all chemical traits showed differences between DCs, but in branches, only N and C showed differences. Mean k was similar between diameters and was greater in DC2 than in DC1. Hemicellulose and P showed positive relations with increasing decomposition, whereas cellulose showed a negative one. Our results support the use of classical DCs. We hypothesize that the presence (or absence) and proportional amount of bark partially explain the differences found between DCs and between diameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".