Assessing the Infectivity of Commercial Mycorrhizal Inoculants in Plant Nursery Conditions
Bibliographic record
Abstract
Abstract The infectivity of ten commercial mycorrhizal inoculants was examined in nursery conditions. Corn plants were grown in a soil-based medium and in two different soilless substrates, a potting mix prepared with redwood bark, pine sawdust, calcined clay and sand, and the commercial Sunshine #5 mix, mainly composed of Canadian sphagnum peat moss. The percentage of mycorrhizal colonization obtained with the different mycorrhizal inoculants ranged from 0 to 50%. This variation might reflect the presence or absence of viable propagules, the difference in the recommended rates applied as well as the difference in the content and type of infective propagules of each product. However, the infectivity of each mycorrhizal inoculant was also influenced by the growing media. Two products promoted higher values of mycorrhizal colonization in the Sunshine mix, three in the nursery mix and one in the soil-based medium. Mycorrhizal colonization did not enhance plant growth. Only the plants inoculated with the products that did not promote mycorrhizal colonization increased their growth relative to the non-inoculated controls, suggesting the presence of other growth promoters in the inoculum products. Based on these results, nurseries should conduct preliminary tests to determine which inoculants will perform in their potting mixes to assure the best fit of inoculum with their particular conditions.
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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) | 0.000 | 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".