Laboratory and Field Studies on Bioremediation of Point-source Contamination in Horticultural Crops Using Biobeds
Bibliographic record
Abstract
Biobeds are technological tools to minimize point-source contamination on productive farms. A four-step workflow for the efficient setting of biobeds in farms and its proof-of-concept is presented: (1) developing a fit-for-purpose pesticide multiresidue analytical method for pesticides in biomatrix; (2) launching biobeds at lab scale for pesticide degradation evaluation; (3) setting up the biobeds in the field; (4) evaluating the pesticide degradation in the biobed during an agricultural year. An ethyl acetate/sodium tetraborate multiresidue method was adapted and validated for 35 pesticides in the biomixture; lab biobeds were installed, and the degradation of 11 pesticides was confirmed. Then, biobeds were installed in two horticultural farms of different productive profiles, considering farmers' conditions, and included in the farmers' routine work. High dissipation rates (∼80%) in both bioreactors were observed for 10 pesticides. This research studies the performance of biobeds in reducing point source contamination and diminishing pesticide concentration from contaminated machinery washings not only at the lab scale but also at in-field experiments performed in productive farms. Moreover, the evaluation of biobeds in actual conditions where different chemical families of pesticides were applied together and the confirmation that repeated applications and accumulation of some compounds throughout the cycle proved biobeds' versatility in diminishing the point of pesticide contamination in farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".