Removal of nutrients from hydroponic greenhouse effluent by alkali precipitation and algae cultivation method
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 BACKGROUND: Hydroponic greenhouse effluent has high concentrations of total phosphorus (30–100 mg PO 4 ‐P L −1 ) and nitrates (200–300 mg NO 3 ‐N L −1 ). Current technologies for effluent treatment have limitations of performance and high maintenance costs. The goals of this study were to investigate strategies which combine alkali treatment and microalgae cultivation for removal of nutrients from hydroponic greenhouse effluent. RESULTS: Treatment with strong alkali was found to effectively remove 97% of total phophorous especially in the form of phosphate, without affecting the nitrate ion concentration in the greenhouse effluent. After alkali treatment, marine algae Dunaliella salina (UTEX 1644) cultivation on treated hydroponic effluent (pH 7.5) showed > 80% decrease in nitrate content in the effluent within 4 days of cultivation. In the same period, the carotene content of the micro‐algal system was in the range 0.5 ± 0.02 µg mg −1 (dry cell weight) which was 1.5 times higher than in the control. CONCLUSION: This study demonstrated that combination of a conventional alkali precipitation method with a microalgae treatment system is a highly efficient approach for the removal of excess nutrients from hydroponic greenhouse effluent in a short treatment time. The microalgae can provide a source of value in the form of carotene. © 2012 Society of Chemical Industry
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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 it