Selenium Volatilization from a Soil—Plant System for the Remediation of Contaminated Water and Soil in the San Joaquin Valley
Bibliographic record
Abstract
Abstract Selenium (Se) contamination of agricultural drainage water is a major environmental problem facing California agriculture. To demonstrate the potential effectiveness of biological volatilization in removing Se from contaminated water and soil, Se volatilization was determined under field conditions from a soil—plant ( Salicornia bigelovii Torr.) treatment system in the San Joaquin Valley, California. Volatile Se was collected using an open‐flow sampling chamber system, biweekly during the S. bigelovii growing season from February to September 1997, and monthly from September 1997 to January 1998. The rate of Se volatilization fluctuated under different field conditions during the study year, with an overall mean of 155 ± 25 µg Se m −2 d −1 . Biological volatilization removed 62 mg Se m −2 yr −1 , which accounted for 6.5% of the annual total Se input (958 mg Se m −2 yr −1 ) to the S. bigelovii field. Forward trajectory analysis showed that the air mass that passed through the research area generally moved quickly out of the San Joaquin Valley within the first 24 h, probably transporting airborne Se from the research site toward the eastern Sierra Nevada in spring and fall, the southern mountainous areas in summer, and the Coast Mountain region in winter. This study suggests that biovolatilization represents an environmentally sound technology for managing Se‐contaminated soil and agricultural drainage water. Future research will focus on establishing new means for enhancing Se volatilization in the field.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".