Microbial signaling and plant growth promotion
Bibliographic record
Abstract
Mabood, F., Zhou, X. and Smith, D. L. 2014. Microbial signaling and plant growth promotion. Can. J. Plant Sci. 94: 1051–1063. The rhizosphere offers a complex microhabitat where root exudates provide a diverse mixture of organic compounds that are used as nutrients or signals by the soil microbial population. On the other hand, these soil microorganisms produce compounds that directly or indirectly assist in plant growth promotion. The widely recognized mechanisms of plant growth promotion are biofertilization, production of phytohormones, suppression of diseases through biocontrol, induction of disease resistance and production of volatile signal compounds. During the past few decades our understanding of the interaction between rhizobacteria and plants has expanded enormously and this has resulted in application of microbial products used as crop inoculants (as biofertilizers), for increased crop biomass and disease suppression. However, this plant–microbe interaction is affected by adverse environmental conditions, and recent work has suggested that inoculants carrying plant-to-bacteria or bacteria-to-plant signals can overcome this and promote plant productivity under stressful environmental conditions. Very recent work has also shown that some plant growth-promoting rhizobacteria secrete novel signaling molecules that also promote plant growth. The use of rhizobacterial signaling in promoting plant growth offers a new window of opportunity, especially when we are looking at plants to provide biofuels and novel bioproducts. Developing technologies that can enhance plant growth and productivity is imperative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".