Determining the effects of biochar and an arbuscular mycorrhizal inoculant on the growth of fowl mannagrass (<i>Glyceria striata</i>) (Poaceae)
Bibliographic record
Abstract
Biochar is gaining attention as an organic soil amendment that can increase plant yields and improve soil fertility. We studied the effect of biochar on the growth of fowl mannagrass ( Glyceria striata (Lam.) Hitchc.) (Poaceae), propagated in a greenhouse for future re-introduction into restored wetlands. Three different application rates (10%, 50%, and 75% biochar volume/substrate volume ( v/ v)) of nutrient-charged (i.e., nutrients added) and uncharged biochar were tested with and without a commercial arbuscular mycorrhizal fungal (AMF) inoculant. Aboveground biomass (shoot mass), belowground biomass (root mass), and shoot height of 166 G. striata samples were recorded after 92 d of growth. Using generalized linear models our data indicated a 50% ( v/ v) application rate of nutrient-charged biochar without AMF produces a significantly greater growth response (4.4× greater shoot height and 85× greater shoot mass compared with 0% biochar (AMF negative) control). We propose that the increased G. striata growth may be due to changes in pH, and (or) increased nutrient availability due to the addition of biochar. We recommend an application rate of 50% biochar ( v/ v) charged with nutrients as an advantageous amendment for propagating G. striata.
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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".