Bibliographic record
Abstract
ABSTRACT Nitric oxide (NO) is a free radical that had been known for many years simply as a toxic air pollutant. The discovery of enzymatic NO production in many living organisms has established a new paradigm: NO being an essential molecule endogenously produced in the cells. In plant science it has been suggested that NO acts as a plant hormone equivalent to ethylene; that is, as a gaseous signal transmitter. Even after experiencing such a scientific breakthrough, however, researchers may still feel difficulty in exploring plant NO signalling systems with conventional approaches. A major difference between plants and animals is that the growth and development of plants is closely linked to the surrounding environment where NO levels vary according to biotic and abiotic activities. This fundamental difference may make the NO‐signalling network system of plants larger and more complicated than that of vertebrates. This review intends to show prospects for the future of NO signalling research in plants by introducing a holistic concept to aid in the exploration of complicated systems such as the plant‐environment system. Furthermore, the novel ONS hypothesis is proposed to encompass the complexity and simplicity of NO in chemistry, biochemistry and physiology.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".