Effluent effects on the nutrient concentrations and growth of reed canarygrass (<i>Phalaris arundinacea</i> L.) and hybrid poplar (<i>Populus deltoides</i> × <i>P. Petrowskyana</i> L.)
Bibliographic record
Abstract
Using effluent as a source of irrigation water and to provide nutrients for plant growth is gaining favour as an environmentally positive practice instead of discharging effluent into surface water bodies. A growth chamber study was conducted to evaluate pulp mill wastewater as an irrigation source. This study evaluated the effects of water (TPW), municipal effluent (ME), and Kraft pulp mill effluent (KPME) and waste activated sludge (WAS) at rates of 1.5, 3, and 6 mm d-1 on available soil nutrients, nutrient uptake, and growth of reed canarygrass (Phalaris arundinacea L.) and hybrid poplar (Populus deltoides × P. petrowskyana L.). Increasing the application rate significantly increased biomass for both crops, but the KPME treatment significantly decreased leaf biomass of the hybrid poplar. Effluent applications did not result in toxic accumulations of nutrients within the analyzed tissues for either reed canarygrass or hybrid poplar. Only the WAS treatment significantly increased soil available concentrations of P, K, S, B, Mn, and Zn. Results of the study found lower irrigation rates of Kraft pulp mill effluents could be used as supplementary source and provide nutrients for reed canarygrass and hybrid poplar. Key words: Biomass, effluent irrigation, hybrid poplar, nutrient concentration, reed canarygrass
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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) | 0.001 | 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 | 0.001 |
| 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".