Plant Species for the Removal of Na+ and Cl– from Greenhouse Nutrient Solution
Bibliographic record
Abstract
Certain ions such as Na + and Cl – can accumulate in recirculating greenhouse nutrient solutions and can reach levels that are damaging to crops. An option for the treatment of this problem is phytodesalinization with Na + and Cl – hyperaccumulating plants that could be added to existing water treatment technologies such as constructed wetlands (CWs). Two microcosm experiments were conducted to evaluate eight plant species including Atriplex prostrata L. (triangle orache), Distichlis spicata (L.) Greene (salt grass), Juncus torreyi Coville. (Torrey’s rush), Phragmites australis (Cav.) Trin. ex Steud. (common reed), Spartina alterniflora Loisel. (smooth cordgrass), Schoenoplectus tabernaemontani (C.C. Gmel.) Palla (softstem bulrush), Typha angustifolia L. (narrow leaf cattail), and Typha latifolia L. (broad leaf cattail) for their Na + and Cl – accumulation potential. An initial (indoor) experiment determined that J. torreyi , S. tabernaemontani , T. angustifolia, and T. latifolia were the best candidates for phytodesalinization because they had the highest Na + and Cl – tissue contents after exposure to Na + and Cl – -rich nutrient solutions. A second (outdoor) experiment quantified the Na + and Cl – ion uptake (grams of each ion accumulated per m 2 of microcosm). J. torreyi , S. tabernaemontani , T. angustifolia, and T. latifolia accumulated 5.8, 3.9, 8.3, and 9.2 g·m −2 of Na + and 25.7, 18.2, 31.6, and 27.2 g·m −2 of Cl – , respectively. Of the eight species, T. latifolia and S. tabernaemontani showed the greatest potential to accumulate Na + and Cl – in a CW environment, whereas S. alterniflora, D. spicata, and P. australis showed the least potential.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".