Assessing the suitability of winery waste bentonite (WB) and winery wastewater (WW) from BC wineries as a soil amendment using plant assays, germination indices, and spectroscopy
Bibliographic record
Abstract
The seasonal generation, high volume, and variable characteristics (high acidity, high salinity, high organic loads) of winery wastes require timely, economical, and sustainable management to promote a circular economy and nutrient cycling in the wine industry. Geoenvironmental engineers have utilized the wastes’ macro- and micro-elemental composition and organic enrichment from wine clarification processes for soil nutritional enhancement and plant growth. Winery bentonite (WB) and winery wastewater (WW) obtained from the local wineries of the Okanagan Valley of British Columbia, Canada were used in different dosages as organic amendments for a commercially available potting mix. Three different plant species- lettuce (L. sativa), wheat (T. aestivum), and tomato (S. lycopersicum) were studied. Germination indices (Germination Index (GI), Mean time to Germinate (MTG), etc), as well as plant growth parameters (height, fresh biomass, fruit count and weight) were evaluated. In addition, Inductively Couple Plasma-Mass Spectroscopy (ICP-MS) test was conducted to analyze the elemental composition of amended soils and plant tissues post-harvest for phytotoxic elements. The germination study shed light on phytotoxicity-induced effects at play, i.e., higher MTG values were seen for WW dosages. Plant growth parameters relayed the positive benefits of an amendment, i.e., increased plant biomass and heights, possibly due to the organic matter (OM) enrichment of the winery wastes. The FTIR-spectra revealed the presence (e.g., polysaccharides (1100-1080 cm⁻¹)) and absence (2850-2920 cm⁻¹ for C-H vibration of methylenes) of some aliphatic peaks. Partial mineralization of the organic matter in winery wastes was evident in the waste or amended soils (e.g., 875 cm⁻¹ for carbonates). Finally, an evaluation and quantification of the trace elements in amended soils relayed concentrations well within allowable limits for all heavy metals, except copper (Cu). For plant tissues, microelements (Mg, Na, P, K, Fe) and heavy metals (Cr, Zn, and especially, Cu) were found in excess compared to the values obtained by studies growing leafy vegetables under natural conditions. Thus, WB and WW would require pretreatments (like filtration or liming of WW, composting of WB, increased drying of WB, or possible removal of copper in WB) to convert it to a completely mineralized, stable and safe organic product.
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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 | 0.001 | 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 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".