Bibliographic record
Abstract
Plants are commonly exposed to large number of different environmental stresses including extremes of pH and temperature, flooding, drought, high salt, both organic and inorganic contaminants, and a variety of pathogenic organisms. As a consequence of these environmental stresses, plants typically synthesize increased levels of the phytohormone ethylene and are often unable to grow and proliferate to any great extent, at least until the stress is removed and the ethylene level is lowered. To reduce the deleterious effects of ethylene stress, plant growth-promoting bacteria (PGPB) that facilitate the proliferation of plants under stress conditions may be added to the system. These bacteria lower the level of growth inhibiting stress ethylene within the plant through the action of the enzyme ACC deaminase, and are also able to directly promote plant growth, usually by providing the plant with the phytohormone indoleacetic. The net result of adding PGPB to plants is a significant increase in both the number of seeds that germinate and the amount of biomass that the plants are able to attain under otherwise stressful and inhibitory conditions. In this chapter we provide a detailed overview regarding the functioning, the biochemistry and the regulation of ACC deaminase, with emphasis on application of PGPB synthesizing this enzyme and supporting plant growth under abiotic stress such as salinity, flooding, drought, organic, and inorganic pollution. Finally, recent developments on the exploitation of transgenic plants expressing ACC deaminase are discussed.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".