Preservation of broadleaf species in Korean pine (<i>Pinus koraiensis</i>) plantations affects soil properties, carbon storage, biomass allocation, and available nitrogen storage
Bibliographic record
Abstract
We analyzed forest floor mass, soil properties, soil organic carbon (SOC) storage, soil available nitrogen (NO3–-N and NH4+-N) (SAN) storage, litter production and decomposition, tree biomass, and the growth rate of Korean pine ( Pinus koraiensis Sieb. et Zucc.) to determine the impacts of keeping broadleaf species in the Korean pine plantation on Korean pine growth and identify the interactions of plants and soil. Forest biomass and litter production were significantly higher in the broadleaf mixed Korean pine plantation (KBP) than in the pure Korean pine plantation (KP). Broadleaf species redistributed carbon from forest floor to mineral soil via its fast litter decomposition rate with the result of a smaller forest floor mass and a greater SOC storage in KBP than in KP. KBP had significantly higher SOC and SAN storages, SOC and SAN concentrations, and pH, and lower soil bulk density than KP. Such differences can be largely explained by the input of broadleaf litter into KBP. The Korean pine in KBP had a greater growth rate and allocated a smaller proportion of biomass below ground, indicating that the broadleaf species influenced the Korean pine growth and biomass allocation pattern by changing soil properties. There was a positive feedback among litter N release rate, SAN storage, and plant growth 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) | 0.001 | 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".