Assessment of Adaptive Capacity of Leaves and Roots of Golf Grass Plant Irrigated by Reclaimed Wastewater
Bibliographic record
Abstract
The actual potential of reclaimed wastewater discharged into the bay of Agadir (south Morocco), is 3.65 million m3/year. This important volume is a significant loss for irrigation in this region characterized by semi-arid climate and a long-suffering of over-exploitation of groundwater. This situation is against to the increasing scarcity of water resources and the quality degradation in the entire region. The production capacity of M’zar plant available for irrigation purpose without any restrictions (category A of WHO norms), is of about 10 000 m3/day and will reach 50 000 m3/day in the medium term. This production capacity of treated wastewaters will fulfill the whole water needs for irrigation of the entire open spaces of Agadir. Our study is focused on the assess of adaptive capacity of leaves and roots of golf grass plant irrigated by reclaimed wastewater. It presents the planning, protocol and the results of tests conducted to evaluate the effects of TWW reuse on the growth of leaves and roots of grass plant. The results presented involve monitoring of fresh and dry weight of leaves and roots during the first 41 days of growth of three varieties of golf grass: Penccross (V1), the Ray Gras English (V2) and a mixture of 60% Ray Gras English and 40% Red Fescue. The results shown that irrigation with TWW has favored the evolution of the fresh and dry weight of leaves than the root of the grass plant. It’s due to nutrients elements content in the TWW and especially nitrogen compared with conventional irrigation with groundwater.
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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) | 0.000 | 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) | 0.001 | 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".