Multi‐Year Effects of Biochar, Lipo‐Chitooligosaccharide, Thuricin 17, and Experimental Bio‐Fertilizer for Switchgrass
Bibliographic record
Abstract
Core Ideas Semi‐Markovian models indicate a positive effect of biochar on switchgrass dry weight. Recursive models show stem number affected fresh weight, which increased dry weight. Switchgrass responds to thuricin 17, an experimental signal compound. Previous research indicated that plant height and tillering contributed to the yield of switchgrass (Panicum virgatum L.) transplants that were spaced at distances of 30 to 107 cm. In Québec, applications of 20 t ha−1 biochar to a sandy soil increased switchgrass biomass fertilized at 100 kg N ha−1. Therefore, the research objectives of this study were to determine whether or not (i) 10 t ha−1 softwood biochar affects traits that contribute to dry weight of fertilized switchgrass; (ii) these traits are affected by: a bacteriocin of Bacillus thurigiensis NEB17 (thuricin 17), a lipo‐chitooligosaccharide of Bradyrhizobium japonicum 532C, and an experimental bio‐fertilizer; and (iii) the path modeling of treatment effects change over time. The experiment was arranged as a randomized complete block design with 11 treatments. For the establishment year, semi‐Markovian path models built on rank‐transformed data indicate that biochar increased dry weight of the aboveground portion of biomass (bBiochar,DW = 0.20 ± 0.15). The data from subsequent years indicate that the stems count per plant directly affected fresh weight (bStems,FW = 0.65 ± 0.09 in 2014 and 0.56 ± 0.11 in 2015), which in turn directly affected dry weight (bFW,DW = 0.89 ± 0.03 in 2014 and 0.83 ± 0.05 in 2015). Thuricin 17 increased the switchgrass height (bT17,Height = 0.26 ± 0.10 based on 2014 and 2015 combined data). In conclusion, biochar can increase switchgrass height and dry weight in the establishment year, thuricin 17 can increase plant height in post‐establishment years, and therefore the effects change over time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".