Modeling in-situ pine root decomposition using data from a 60-year chronosequence
Bibliographic record
Abstract
Because the root system of a mature pine tree typically accounts for 2030% of the total tree biomass, decomposition of large lateral roots and taproots following forest harvest and re-establishment potentially impact nutrient supply and carbon sequestration in pine systems over several decades. If the relationship between stump diameter and decomposition of taproot and lateral root material, i.e., wood and bark, can be quantified, a better understanding of rates and patterns of sequestration and nutrient release can also be developed. This study estimated decomposition rates from in-situ root systems using a chronosequence approach. Nine stands of 55- to 70-year-old loblolly pine (Pinus taeda L.) that had been clear-cut 0, 5, 10, 20, 25, 35, 45, 55, and 60 years ago were identified on well-drained Piedmont soils. Taproot and lateral root systems were excavated, measured, and weighed. Although more than 50% of the total root mass decomposed during the first 10 years after harvest, field excavations recovered portions of large lateral roots (>5 cm diameter) and taproots that persisted for more than 35 and 60 years, respectively. Results indicate that decomposition of total root biomass, and its component parts, from mature, clear-cut loblolly pine stands, can be modeled with good precision as a function of groundline stump diameter and years since harvest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".