Nitrogen dynamics and red pine growth following application of pelletized biosolids in Massachusetts, USA
Bibliographic record
Abstract
Pelletized biosolids from municipal wastewater treatment were applied to a thinned red pine plantation to determine if there were treatment levels that could produce a fertilization growth response without resulting in unacceptable nitrate leaching. The pellets (total nutrient concentrations of 4.4% N, 1.4% P, 0.2% K) were applied at four levels (0, 200, 400, 800 kg/ha total N). Only 26% of labile organic N in the pellets was mineralized in the first year after application. Foliar N increased with increasing application rate, but other nutrients were unchanged. Red pine basal area growth was unchanged with low and medium levels, but decreased to 50% of control plots with the highest level. A decrease in foliar K/N ratio resulting from high N uptake with little additional K is hypothesized as the cause for the growth decline. The highest treatment level resulted in lysimeter nitrate-N concentrations increasing to 2 mg/L in the first year and 9 mg/L in the second, returning to control levels in the third; no increase occurred in other treatments. These results contrast with those found with liquid sludge applications, in which nearly all N mineralization and the highest nitrate leaching rates occurred in the first year. The slower release of inorganic N from pellets over 2 years may allow higher total N application rates without causing high nitrate leaching.
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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".