Irrigation with primary wastewater alters wood anatomy and composition in willow Salix miyabeana SX67
Bibliographic record
Abstract
Traditional treatment of wastewaters is a burden for local governments. Using short rotation coppice willow (SRCW) as vegetal filter has several environmental and economic benefits. Here, we investigated the effect of primary wastewater irrigation on wood structure and composition of the willow cultivar Salix miyabeana ‘SX67’ following two years of growth. Compared to unirrigated plants (UI), stem sections of plants irrigated with primary wastewater (WWD) showed an unexpected decrease of hydraulic conductance (KS) associated with a decrease in vessel density but not vessel diameter. The majority (86%) of vessels had diameters range groups [20-30[, [30-40[and [40-50[µm and contributed to > 75% of theoretical KS, while the group class [50-60[µm (less than 10% of vessels) still accounted for > 20% of total KS regardless irrigation treatments. WWD significantly alters the chemical composition of wood with an increase of glucan content by 9 to 16.4% and a decrease of extractives by 35.3 to 36.4% when compared to UI or to plants irrigated with potable water (PW). The fertigation did also increase the proportion of the tension wood which highly correlated with glucan content. In the context of energetic transition and mitigation of climate change, such results are of high interest since WWD effectively permit the phytofiltration of large amounts of organic contaminated effluents without impairing SRCW physiology.
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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 | 0.001 |
| 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".