Decomposition and nutrient release from fresh and dried pine roots under two fertilizer regimes
Bibliographic record
Abstract
Root decomposition and nutrient release are typically estimated from dried root tissues; however, it is unlikely that roots dehydrate prior to decomposing. Soil fertility and root diameter may also affect the rate of decomposition. This study monitored mass loss and nutrient concentrations of dried and fresh roots of two size classes (<2 and 2–5 mm) over a 12-month period in fertilized and control plots in a 13-year-old loblolly pine (Pinus taeda L.) plantation. Nutrient content was calculated and used to assess the effects of fertilization, root size, and initial condition (hydration) on nutrient release rates. Roots that grew and decomposed in fertilized plots had higher concentrations and greater total release of N, P, K, and Mg than roots in control plots, but C concentrations and mass loss rate were not significantly different between roots in fertilized plots and those in control plots. Very fine roots (<2 mm) had higher concentrations of N, P, and Ca and faster release rates for C, N, and K than fine roots (2–5 mm), resulting in greater total release of C and N. Roots dried prior to decomposition decayed and released C, K, Ca, and Mg at a faster rate than fresh roots. Results indicate that using dried root tissues will overestimate fine root decomposition and nutrient cycling rates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".