Effects of addition of flavonoid signals and environmental factors on nodulation and nodule development in the pea ( <i>Pisum</i> <i>sativum</i> ) <i>–Rhizobium</i> <i>leguminosarum</i> bv. <i>viciae</i> symbiosis
Bibliographic record
Abstract
The legume– Rhizobium symbiosis is the most important source of biologically fixed nitrogen in agricultural systems. However, it is complex and sensitive to environmental effects, including available mineral nitrogen, soil salinity, and low root-zone temperature. How these factors inhibit the symbiosis is not well understood. If the effects are mostly on the early stages of nodulation, addition of signal molecule(s) may overcome it. Pisum sativum seeds were germinated and the seedlings were inoculated with bacterial culture and cultivated under controlled environment conditions, studying each of the above nodulation-inhibitory factors, under 3 levels: control (little or no inhibitory condition), and moderately or severely inhibitory conditions. Aspects of nodule development (size and number) were measured with a scanner-based technology. All of the environmental conditions studied had effects on both nodule establishment and development. The addition of either hesperitin or naringenin frequently modified nodule development, most markedly under saline conditions. Flavonoid additions had only small effects under high nitrogen availability conditions, and stronger effects under saline and low root zone temperature 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 | 0.000 | 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".