Comparison of agri-environmental phosphorus tests for boreal agricultural and natural Podzols
Bibliographic record
Abstract
Over a dozen soil phosphorus (P) extraction procedures have been designed for agri-environmental purposes (P-tests). Sustainable expansion of agriculture into boreal regions dominated by Podzols requires further insights into P extractability. We extracted P from Podzol samples ( n = 96) using nine P-tests followed by both colorimetric (PCol) and inductively coupled plasma (PICP) quantifications and assessed the relationships between P-tests. Samples were collected by depth or horizon from agricultural fields and reference sites in eastern, central, and western Newfoundland, Canada. The soil P was extracted with water, citric acid, ammonium bicarbonate diethylenetriaminepentaacetic acid (AB-DTPA), Morgan, Olsen, Bray-1, Bray-2, Mehlich-1, and Mehlich-3 solutions, thus targeting a wide range of extractable P pools in managed and natural Podzols. The soils had a pH of 3.4–6.9, organic matter of 0.5%–47.2%, and Al-M3 of 977–2561 mg kg−1. On average, water extracted the lowest PCol (1.0) and PICP (5.7) mg kg−1, while citric acid extracted the highest PCol (151) and PICP (290) mg kg−1. For the managed podzolic soils, the extractability of P followed the sequence water < Morgan < AB-DTPA < Mehlich-1 < Bray-1 < Mehlich-3 ≤ Olsen ≤ Bray-2 < citric acid; this varied slightly by quantification techniques and soil groups. The differences between PICP and PCol were most significant for the citric acid extracts. Most P-tests measurements were moderately to strongly correlated to P-M3ICP measurements ( r2 > 0.50) but variable with quantification techniques and soil depths. Given the diversity in extractable P pools across management-induced soil conditions, it is evident that a fully informed P management for the Newfoundland Podzols will require calibration of P-tests against crop P uptake.
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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 | 0.001 |
| 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 | 0.001 | 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".