Potential Use of Two Forest Species (Salix alba and Casuarina glauca) in the Rhizofiltration of Heavy-Metal-Contaminated Industrial Wastewater
Bibliographic record
Abstract
The discharge of raw industrial wastewater (IWW) into ecosystems is a major environmental problem that adversely affects water quality, soil physicochemical properties, the food chain and, therefore, human health. Injection of treated IWW into irrigation and “fertigation” systems is an ecological, sustainable and economical approach for its appropriate disposal. Seedlings of two forest species (Salix alba, Casuarina glauca) were grown hydroponically and subjected to 25% diluted IWW and control (tap water) treatments for 35 days. Morphological and physiological traits were evaluated, including leaf symptoms, stem and root dry masses, leaf water potential, relative water content, chlorophyll content, photosystem II efficiency, hydrogen peroxide, thiobarbituric acid reactive substances, bioaccumulation and translocation factor estimates and removal efficiency for various heavy metals. Application of 25% IWW stress affected many aspects of plant morphology: chlorosis and necrosis in leaves, epinasty, leaf curling, early leaf senescence and root browning. In both species, the 25% IWW treatment reduced leaf, stem and root dry masses relative to controls. S. alba exhibited greater removal capacity for heavy metal ions and could be effective as a remediator of toxic-metal-polluted industrial effluent water.
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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 | 0.000 | 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 | 0.000 | 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 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".