Chemical retardation of phosphate diffusion in simulated acid soil amended with lignosulfonate
Bibliographic record
Abstract
The availability and movement of inorganic phosphate fertilizer is usually low due to precipitation and adsorption reactions in soil. Lignosulfonate (LS), which is produced from acid sulfite pulping processes, has similar characteristics to soil organic materials. An experiment was designed to study the effects of LS on P movement in a simulated acid soil containing aluminum-saturated cation exchange resin and acid-washed fine sand. The resulting simulated soil had a cation exchange capacity of 22 cmolc kg−1 and either no or 10 g kg−1 gibbsite. Movement of surface-applied monopotassium phosphate was studied in soil columns, either with 20 g kg−1 LS or without LS. Lignosulfonate reduced phosphate fixation and sustained a higher water extractable phosphate concentration near the surface of the columns, but had no effect on downward phosphate movement in the columns with gibbsite. Lignosulfonate reduced the solution concentration of P near the surface and reduced downward phosphate movement in the columns without gibbsite. The resin-sand column with gibbsite closely reflected an acid soil, and this research showed that adding LS would increase fertilizer P availability in a gibbsite-rich acid soil. Adding Ca-LS to Al-rich soil is beneficial for another reason, improving Ca nutrition, which is poor for these soils. Key words: Chemical retardation, phosphate diffusion, lignosulfonate amendment
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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) | 0.001 | 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".