Effects of inoculation with different Rhizobium leguminosarum strains on growth performances, yield, and grain quality of a semi-leafless pea variety.
Bibliographic record
Abstract
In order to study the effects of inoculation with Rhizobium leguminosarum strains on growth performances,yield,and grain quality of Canadian pea M.P.1824,a pot experiment was conducted involving inoculating with eight different Rhizobium leguminosarum strains.The results showed that,compared with no-inoculated control,rhizobium inoculation could extend pea's growth periods,produce a better growth in pea's ripening period,increase yield,and increase grain crude protein and fiber content,but reduce the content of soluble sugar.When inoculated with rhizobium ACCC 16058,periods of flowering beginning,flowering and podding beginning,podding,pod ripening delayed by 3.1,5.2,4.8,and 12 d,respectively.The number of branches at flowering and podding beginning period and plant height at pod ripening period increased by 45.5% and 16.61% respectively.In addition,pods per plant,grains per plant,grain dry weight per plant,and grain dry weight per pot were significantly(P0.05) increased by 76.67%,85.54%,65.13%,and 36.71%,respectively.Inoculating with commercial rhizobium F98 significantly(P0.05) raised the crude protein content and reduced the soluble sugar content,with variable quantity of +12.37% and-37.77%.Inoculating with CCBAU 43228 and CCBAU 43232 significantly(P0.01) increased the content of crude fiber by 1.24 and 1.20 times compared with CK,and by 39.7% and 37.6% compared with F98.While inoculating with CCBAU 43232 decreased the content of soluble sugar by 35.65%.In conclusion,for pot experiment,the comprehensive performance of M.P.1824 inoculated with rhizobium ACCC 16058 was the best.
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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) | 0.001 | 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".