Long-Term Soil Productivity: genesis of the concept and principles behind the program
Bibliographic record
Abstract
The capacity of a forest site to capture carbon and convert it into biomass defines fundamental site productivity. In the United States, the National Forest Management Act (NFMA) of 1976 mandates that this capacity must be protected on federally managed lands. Responding to NFMA, the USDA Forest Service began a soil-based monitoring program for its managed forests. Lacking an extensive research base, soil-based standards were predicated largely on professional judgment. To provide a stronger foundation, a national program of Long-Term Soil Productivity (LTSP) research was established. The LTSP program addresses both short- and long-term consequences of site and soil disturbance on fundamental forest productivity. Research centers on two key properties affecting a site's long-term productive capacity, site organic matter and soil porosity, each of which is readily influenced by management. A coordinated research network of more than 100 field installations in the United States and Canada is examining how pulse changes in these properties affect soil processes supporting vegetative growth and potential productivity. Results from installations with ≥5 years of response were presented on the 10th anniversary of LTSP, and the latest findings are assembled here. This paper describes the evolution of the study and the characteristics of the oldest field installations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".