An evaluation of extractants for assessment of metal phytoavailability to guide reclamation practices in acidic soilscapes in northern regions
Bibliographic record
Abstract
Abedin, J., Beckett, P. and Spiers, G. 2012. An evaluation of extractants for assessment of metal phytoavailability to guide reclamation practices in acidic soilscapes in northern regions. Can. J. Soil Sci. 92: 253-268. Although soil organic matter and nutrient bioavailability in metal-impacted soilscapes of Sudbury, Ontario, are potentially limiting full ecological recovery, total metal content was used as the critical driver for a 2008 ecological risk assessment. The current greenhouse study evaluated chemical extractants to predict bioavailability of nutrients and contaminant metals to indigenous grasses (Deschampsia). Single extraction methods (0.01 M strontium nitrate, water, 0.01 M calcium chloride, 0.1 M sodium nitrate, 1.0 M ammonium nitrate, 0.1 M lithium nitrate, 1.0 M magnesium chloride, 0.11 M acetic acid, 1.0 M ammonium acetate, 0.05 M ammonium-EDTA, pore water) were examined to assess availability of potentially phytotoxic metals and nutrients in smelter-impacted soils. Extraction procedures to predict phytoavailability were either soil concentration or plant tissue concentration and element dependent. Total and extractable metal concentrations were more correlated for regional contaminant metals (e.g., copper, lead, arsenic, selenium) released by the smelting industry than non-contaminant ones (e.g., iron, calcium, potassium, boron, zinc, molybdenum). The lack of relationship between total and extractable concentrations for most non-contaminant metals suggests total concentration is not a good indicator of phytoavailability for nutrient elements. Stronger correlations between shoot tissue and extractable concentrations were observed for less aggressive extractants (pore water, water, lithium nitrate) reflecting their suitability in predicting phytoavailability over most aggressive ones (except ammonium nitrate).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".