Relationships between net nitrogen mineralization, properties of the forest floor and mineral soil, and wood production in <i>Pinus radiata</i> plantations
Bibliographic record
Abstract
We examined the relationship between net nitrogen (N) mineralization (subsequently termed N mineralization) in the forest floor and mineral soil (00.15 m) of 20 Pinus radiata D. Don plantations ranging in age from 23 to 59 years, how mineralization was influenced by soil properties, and its relationship to wood production. Forest floor properties had a narrower relative range than the same set of mineral soil properties. Total N in the litter layer was 5.09.5 g·kg1 compared with 0.232.53 g·kg1 in mineral soil. Laboratory rates of net N mineralization ranged between 1.1 and 9.7 mg·kg1·day1 in forest floor and between 0.02 and 0.53 mg·kg1·day1 in mineral soil. The range in litter lignin (35.348.0%) was especially narrow, despite the large range in stand productivity. Nitrogen mineralized in the forest floor was not correlated with any of the measured forest floor or mineral soil properties. Nitrogen mineralized per unit mineral soil N (ksn) was negatively correlated with the mineral soil N to organic phosphorus ratio (N/Po) (r2 = 0.82). In mineral soil a relationship combining N/Po and total N concentration explained 90% of the variation in N mineralized. Nitrogen mineralized in the forest floor was correlated with that mineralized in the mineral soil when expressed per unit C or N (r2 = 0.54 or 0.57, respectively). Thus, the quality of organic matter in the forest floor partly reflected the quality of organic matter in the mineral soil with respect to N mineralization. Mineralization in mineral soil dominated the net N available to the stand. For sandy soils, wood production (m3·ha1·year1) was correlated with N mineralized in the forest floor + mineral soil (r2 = 0.71). In P. radiata stands growing in southern Australia, rates of wood production per unit N mineralized and per unit rainfall appear to be substantially higher than those of a wide range of natural and planted stands in North America.
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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".