Relationship of soil phosphorus fractions to phosphorus soil tests and fertilizer response
Bibliographic record
Abstract
Soil tests for available P may not be accurate because they do not measure the appropriate P fraction in soil. A sequential extraction technique (modified Hedley method) was used to determine if soil test P methods were accurately assessing available pools and if predictions of fertilizer response could be improved by the inclusion of other soil P fractions. A total of 145 soils were analyzed from field P fertilizer experiments conducted across Alberta from 1991 to 1993. Inorganic P (Pi) removed by extraction with an anion-exchange resin (resin P) was highly correlated with the Olsen and Kelowna-type soil test P methods and had a similar relationship with P fertilizer response. No appreciable improvement in the fit of available P with P fertilizer response was achieved by including any of the less available P fractions in the regression of P fertilizer response with available P. Little Pi was extractable in alkaline solutions (bicarbonate and NaOH), particularly in soils from the Brown and Dark Brown soil zones. Alkaline fractions were the most closely related to resin P, but the relationship depended on soil zone. Inorganic P extractable in dilute HCl was most strongly correlated with soil pH, reflecting accumulation in calcareous soils, while Pi extractable in concentrated acids (HCl and H2SO4) was most strongly correlated with clay concentration. A positive but weak relationship as observed between these fractions and resin P. Complete fractionation of soil P confirmed that soil test P methods were assessing exchangeable, plant-available P. Key words: Hedley phosphorus fractionation, resin, Olsen, Kelowna
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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 | 0.002 |
| 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".