Selenium Volatilization from a Soil—Plant System for the Remediation of Contaminated Water and Soil in the San Joaquin Valley
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 it