Understanding soil nutrients and characteristics in the Pacific Northwest through parent material origin and soil nutrient regimes
Bibliographic record
Abstract
A convenient method is necessary for assessing the availability of soil nitrogen in plantation Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stands in the Pacific Northwest. The objective of this research was to use soil parent materials (SPMs) and soil nutrient regimes (SNRs) to determine the most efficient method to characterize soil nitrogen availability in Douglas-fir stands. It was hypothesized that SPMs and SNRs would effectively separate stands with distinctive climate, site, and soil characteristics and forest floor and soil carbon and nitrogen reserves. At 60 Douglas-fir stands, SPMs and SNRs were determined, and soil particle percentages, soil depth, and forest floor and soil nitrogen and carbon contents were measured to a depth of 1 m. Soils of sedimentary origin and very rich and rich SNRs contained greater nitrogen and carbon contents than those from glacial and igneous origins and medium SNRs. Sedimentary SPMs and very rich SNRs were developed from older parent materials and had significantly greater soil depths and finer textures than those from glacial SPMs and medium SNRs. SNRs and SPMs are recommended as good estimators of soil nutrient pools and soil characteristics in Douglas-fir plantation forests of the Pacific Northwest.
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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 | 0.001 | 0.001 |
| 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".