Phytoavailability of Cu and Zn to lettuce (<i>Lactuca</i> <i>sativa</i>) in contaminated urban soils
Bibliographic record
Abstract
This study compares the effectiveness of several chemical evaluation procedures to predict Cu, and Zn concentrkjghgfsdations in lettuce (Lactuca sativa 'Buttercrunch') grown on contaminated soils from the Montreal urban area. The plant growth assays were performed in the greenhouse using field-collected, non-spiked soils. The soils were characterized using several chemical extraction reagents, as well as electrochemical speciation of the soil solution free metal species. The chemical characterization was supplemented with labile metal pool determinations using anion exchange membranes treated with DTPA or EDTA. The results show that the more sophisticated electrochemical speciation and exchange resins procedures did not consistently improve predictions of metal uptake. We believe this is due to the minimization of metal solubility caused by the circumneutral or alkaline pH values and the relative homogeneity of the relatively small urban soil sample set that we used. The metal solubility and bioavailability in the soils tested were minimized by the particular chemical properties of the soils, obscuring any potential advantages from more discriminate soil chemical evaluation procedures. Nevertheless, the reported regressions (for 10 different methods) are valid estimates of Cu and Zn phytoavailability in contaminated urban soils. Key words: Lettuce, trace metals, bioavailability, chemical speciation, ion exchange membranes, contaminated soils, free ion
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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) | 0.000 | 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) | 0.000 | 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".