Root Characteristics of Winter Wheat as Affected by Co-application of Biosolids and Water Treatment Residuals
Bibliographic record
Abstract
Co-application of water treatment residuals(WTR) with biosolids can reduce the buildup of P in soil as well as the risk of P losses to surface and ground water.However,co-application of WTR and biosolids may result in P deficiency in soil and plant AI toxicity with increasing WTR rate.In this study,their co-application effects on the growth of winter wheat(Triticum aestivium L.) were studied in terms of morphological characteristics of plant roots in a greenhouse experiment.Treatments included WTR alone(0,80 g·kg-1 soil) and combinations of biosolids at 50 g·kg-1 soil with WTR at 0,10,40,and 80 g·kg-1 soil,respectively.Biosolids addition increased root length(RL),root surface area(RSA),root volume(RV) and root length density(RLD) by 139.4 %,140.1 %,157.6 % and 139.4 %,respectively.Increasing WTR application rate did not result in adverse effects on all root parameters [RL,RSA,RD(root average diameter),RV,RLD and SRL(specific root length)] within its current application range.This indicated co-application of WTR with biosoilds can be a promising practice for agricultural production when reducing adverse impacts on environmental quality.
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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) | 0.000 | 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".