A<sup>31</sup>P-NMR spectroscopic study of phosphorus forms in two phosphorus-fertilized grassland soils in eastern Canada
Bibliographic record
Abstract
Phosphorus (P) fertilization can increase grassland production, but will also alter P forms, changing their cycling and potential for loss in runoff. We assessed the effects of mineral P fertilization on soil P forms in timothy swards at two sites in Quebec, Canada. Soil samples (10 cm depth) were collected in autumn 2013 from replicate plots at Lévis on a Kamouraska clay and at Normandin on a Labarre clay loam, each having received three rates of triple superphosphate (0, 20, and 40 kg P ha−1) for 4 yr. These were analyzed for pH, total carbon (TC), total nitrogen (TN), and total phosphorus (TP); Mehlich-3-extractable aluminium (AlM3), iron (FeM3), calcium (CaM3), and P (PM3); and 31P nuclear magnetic resonance spectroscopy (31P-NMR) following sodium hydroxide–ethylenediaminetetraacetic acid (NaOH–EDTA) extraction. Phosphorus fertilization had no significant effects on soil TC, TN, AlM3, FeM3, CaM3, and pH, but significantly increased TP, NaOH–EDTA-extractable total P and total inorganic P, PM3, orthophosphate, and glucose 6-phosphate at both sites. In contrast, NaOH–EDTA-extractable total organic P, total orthophosphate diesters, and scyllo-inositol hexaphosphate decreased with P fertilization. Phosphorus fertilization over 4 yr increased soluble inorganic P and decreased organic P at both grassland sites.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".