Effect of <i>Penicillium bilaii</i> inoculation and phosphorus fertilisation on root and shoot parameters of field-grown pea
Bibliographic record
Abstract
The mechanisms underlying the stimulating effect of Penicillium bilaii inoculation on the growth of crop plants are not clear. The effect of P. bilaii inoculation on root morphology [dry weight (DW), length and specific root length) and other root and shoot parameters of pea were investigated at two field sites in western Canada in 1996. Pea (Pisum sativum L.) was grown at three levels of P fertilization (0, 6.4 and 19.3 kg ha–1) at Ellerslie, AB, and Outlook, SK. Shoot dry matter, P concentrations, P content and nodulation were also measured in pea plants harvested at the seven- to nine-node stage. Soil cores were used to sample roots. Although plants were responsive to P fertiliser at both sites, P. bilaii inoculation affected pea growth at the Ellerslie site only. At this site, P. bilaii inoculation increased root length by 48%, specific root length (m g–1 root DW) by 21%, root DW by 13%, and shoot P concentration by 13% in treatments that received no P fertiliser. We suggest that the stimulating effect of P. bilaii inoculation on crop P nutrition is partly explained by increases in root absorptive capacity in the presence of the fungus. However, this effect was only significant under P-limited conditions. Key words: Microbial inoculants, Pisum sativum, Penicillium bilaii, phosphorus, root length, root morphology
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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".