Can we simultaneously decontaminate and cultivate? An urban cherry tomato story
Bibliographic record
Abstract
Abstract Urban planners are increasingly focused on integrating urban farming as a creative strategy for producing sustainable, local food in underutilized spaces, once occupied by polluting industries, in peri‐urban areas. However, before urban farming's contribution to local self‐sufficiency can be determined, it is crucial to gather quantitative data on the effectiveness of tailored strategies, including polyculture, and the suitability of different vegetables, to adequately assess the value of brownfields. This study aims to provide quantitative agronomic data on a copper‐contaminated urban garden with the dual objective of assessing its capacity for food production and mitigating the spread of soil contamination. Tomato ( Solanum lycopersicum ) plants were grown for one growing season in brownfield soils with different copper concentrations (up to 2000 mg kg −1 ), accompanied by different plant species assemblages typically used in phytoremediation ( Achillea millefolium , Salix discolor , and Trifolium repens ). The most successful assemblage yielded over 800 fruits per square meter with low copper concentrations (<5 mg kg −1 ), indicating that one square meter could satisfy the annual fresh tomato weight requirement of an average individual. Although some amendments can improve adaptability to local soils, the highest fruit‐producing assemblage consisting of S. lycopersicum , S. discolor , and T. repens also proved to be the one of the most effective for copper phytoextraction, removing over 30 g ha −1 from the contaminated soils. Overall, the data indicated that all assemblages phytostabilized copper. The quantitative results of this study provide a valuable benchmark for urban planners and researchers to implement large‐scale urban agriculture on brownfield sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".