Bibliographic record
Abstract
This paper reviews how terrestrial plants reduce photorespiration and thus compensate for its inhibitory effects. As shown in the equation φ = (1/Sc/o )O/C, where φ is the ratio of oxygenation to carboxylation, Sc/o is the relative specificity of Rubisco, O is stromal O2 level and C is the stromal CO2 concentration, plants can reduce photorespiration by increasing Sc/o or C, or by reducing O. By far the most effective means of reducing φ is by concentrating CO2, as occurs in C4 and CAM plants, and to a lesser extent in plants using a glycine shuttle to concentrate CO2 into the bundle sheath. Trapping and refixation of photorespired CO2 by a sheath of chloroplasts around the mesophyll cell periphery in C3 plants also enhances C, particularly at low atmospheric CO2. O2 removal is not practical because high energy and protein investment is needed to have more than a negligible effect. Sc/o enhancement provides for modest reductions in φ, but at the potential cost of limiting the kcat of Rubisco. An effective means of decreasing φ and enhancing carbon gain is to lower leaf temperature by reducing absorbance of solar radiation, or where water is abundant, opening stomata. By using a combination of mechanisms, C3 plants can achieve substantial (>30%) reductions in φ. This may have allowed many C3 species to withstand severe competition from C4 plants in low CO2 atmospheres of recent geological time, thereby preserving some of the Earth's floristic diversity that accumulated over millions of years.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".