Comparisons of Various Chemical Extracts as Quantity Factors to Determine Metal-Buffering Capacity of Soils
Bibliographic record
Abstract
Applicability of various chemical extracts was investigated as quantity (Q) factors to determine cadmium (Cd), zinc (Zn), and copper (Cu) desorption quantity–intensity (Q/I) relationships in soils. The metal extracts were sums of sequential metal fractions (except the residual fraction) using Tessier's (TSE) and Community Bureau of Reference (BCR) procedures and various single chemical extracts: 1.0 M potassium nitrate (KNO3), 1.0 M magnesium nitrate [Mg(NO3)2], 1.0 M magnesium chloride (MgCl2), and 0.11 M acetic acid (CH3COOH) solutions. Water-extractable metal was applied as a fixed intensity (I) factor. The TSE or BCR metal fractions were significantly correlated with diethylenetriaminepentaacetic acid (DTPA)–extractable metals, and all the metal desorption Q/I curves were linearly fitted. However, most of metal BC values estimated by using the single chemical extracts were very low and did not have consistent trends for target metals. Only 0.11 M CH3COOH-extractable metals might be reliable. Therefore, TSE and BCR metal fractions can be applicable to replace DTPA-extractable metals, and 0.11 M CH3COOH-extractable metals might also be useful.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".