Bibliographic record
Abstract
1. Cellular architecture - regulation of cell size, cell shape, and organ initiation. Andrew Fleming, Department of Animal and Plant Sciences, University of Sheffield, UK. 2. Leaf architecture - regulation of leaf position, shape, and internal structure. Julie Kang and Nancy G. Dengler, Department of Botany, University of Toronto, Canada. 3. Shoot architecture I - regulation of stem length. John J. Ross, James B. Reid, James L. Weller and Gregory M. Symons, School of Plant Science, University of Tasmania, Hobart, Australia. 4. Shoot architecture II - control of branching. Colin G. N. Turnbull, Department of Agricultural Sciences, Imperial College London, Wye Campus, UK. 5. Floral architecture - regulation and diversity of floral shape and pattern. Elena M. Kramer, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA. 6. Inflorescence architecture. Anuj M. Bhatt, Department of Plant Science, University of Oxford, UK. 7. Root architecture. J. Lopez-Bucio, A. Cruz-Ramirez, A. Perez-Torres, J. G. Ramirez-Pimentel, L. Sanchez- Calderon and L. Herrera-Estrella, Departamento de Ingenieria Genetica, Centro de Investigacion y Estudios Avanzados, Guanajuato, Mexico. 8. Woody tree architecture. Frank Sterck, Silviculture and Forest Ecology Group, Department of Environmental Science, University of Wageningen and Research Center, Netherlands. 9. Plant architecture modelling - virtual plants and complex systems. Christophe Godin, INRIA, Montpellier, France and Evelyne Costes and H. Sinoquet, INRA, Montpellier, France. 10. Applications of plant architecture. Nick Battey, School of Plant Sciences, University of Reading, UK. References. Index
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 | 0.000 | 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 | 0.000 | 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".