Phytoremediation of Cu‐contaminated vineyard soils in Brazil: A compendium of Brazilian pot studies
Bibliographic record
Abstract
Abstract Vineyard soils can be contaminated by copper (Cu) due to successive applications of fungicides and organic fertilizers. Soil remediation can be addressed by altering soil properties or selecting efficient Cu‐extracting cover crops tolerant to Cu toxicity. Our objectives were to synthesize the Cu‐extracting efficiency by plant species tested in Brazil, classify them according to Cu resistance to toxicity, and assess the effect of soil properties on attenuating Cu toxicity. We retrieved results from 41 species and cultivars, totaling 565 observations. Freshly added Cu varied between 50 and 600 mg Cu kg−1 of soil across studies. The partition of Cu removal between the above‐ and below‐ground portions was scaled as a logistic variable to facilitate data synthesis. The data were analyzed using the Adaboost machine learning model. Model accuracy (predicted vs. actual values) reached R2 = 0.862 after relating species, cultivar, Cu addition, clay, SOM, pH, soil test P, and Cu as features to predict the logistic target variable. Tissue Cu concentration varied between 7 and 105 mg Cu kg−1 in the shoot and between 73 and 1340 mg Cu kg−1 in the roots. Among soil properties, organic matter and soil test Cu most influenced the accuracy of the model. Phaseolus vulgaris, Brassica juncea, Ricinus communis, Hordeum vulgare, Sorghum vulgare, Cajanus cajan, Solanum lycopersicum, and Crotolaria spectabilis were the most efficient Cu‐extracting cover crops, as shown by positive values of the logistic variable (shoot removal > root removal). Those Cu‐tolerant plants showed differential capacity to extract Cu in the long run.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".