A comparison of sole carbon source utilization patterns and phospholipid fatty acid profiles to detect changes in the root microflora of hydroponically grown crops
Bibliographic record
Abstract
Sole carbon source utilization (SCSU) patterns and phospholipid fatty acid (PLFA) profiles were compared with respect to their potential to characterize root-inhabiting microbial communities of hydroponically grown crops. Sweet pepper (Capsicum annum cv. Evident), lettuce (Lactuca sativa cv. Grand Rapids), and four different cultivars of tomato (Lycopersicon esculentum cvs. Gitana, Armada, Aromata, and Elin) were grown in 1-L black plastic beakers placed in a cultivation chamber with artificial light. In addition to the harvest of the plants after 6 weeks, plants of one tomato cultivar, cv. Gitana, were also harvested after 4 and 8 weeks. The cultivation in this study was performed twice. Principal component analysis was used to analyze the data. Both characterization methods had the ability to discriminate between the root microflora of different plant species, cultivars, and one tomato cultivar at different ages. Differences in both SCSU patterns and PLFA profiles were larger between plant species than between cultivars, but for both methods the largest differences were between the two cultivations. Still, the differences between treatments were always due to differences in the same PLFAs in both cultivations. This was not the case for the SCSU patterns when different plant ages were studied. Furthermore, PLFA profiles showed less variation between replicates than did SCSU patterns. This larger variation observed among the SCSU data indicates that PLFA may be more useful to detect changes in the root microflora of hydroponically grown crops than the SCSU technique.
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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 | 0.001 | 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) | 0.001 | 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".