Bibliographic record
Abstract
Ever since the discovery of C(4) photosynthesis in the mid-1960s, plant biologists have envisaged the introduction of the C(4) photosynthetic pathway into C(3) crops such as rice and soybeans. Recent advances in genomics capabilities, and new evolutionary and developmental studies indicate that C(4) engineering will be feasible in the next few decades. Furthermore, better understanding of the function of C(4) photosynthesis provides new ways to improve existing C(4) crops and bioenergy species, for example by creating varieties with ultra-high water and nitrogen use efficiencies. In the case of C(4) engineering, the main enzymes of the C(4) metabolic cycle have already been engineered into various C(3) plants. In contrast, knowledge of the genes controlling Kranz anatomy lags far behind. Combining traditional genetics, high-throughput sequencing technologies, systems biology, bioinformatics, and the use of the new C(4) model species Setaria viridis, the discovery of the key genes controlling the expression of C(4) photosynthesis can be dramatically accelerated. Sustained investment in the research areas directly related to C(4) engineering has the potential for substantial return in the decades to come, primarily by increasing crop production at a time when global food supplies are predicted to fall below world demand.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".