New insights on cycad biology and evolution
Bibliographic record
Abstract
AUTHORS:Salzman, Shayla1, M.R.L. Whitaker2, A.M. Sierra3, E.D Bustos-Díaz4, A. Cibrián-Jaramillo5, Y. Liu6, F. Barona-Gómez4, S.Z. Zhang6, S.K. Sahu7,9, J.C. Villarreal A.3,8,91 University of Georgia, Entomology, Athens, GA, US2 East Tennessee State University, Biological Sciences, Johnson City, TN, US3 Département de Biologie, Université Laval, G1V 0A6, Québec, Canada4 Institute of Biology, University of Leiden, 2333 BE, Leiden, NL.5 Naturalis Biodiversity Center, Darwinweg 2 2333 CR Leiden, The Netherlands6 Shenzhen & Chinese Academy of Sciences, Fairy Lake Botanical Garden, Shenzhen, CN7 State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen 518083, China8 Smithsonian Tropical Research Institute, Ancón, Panamá9 Corresponding authors
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".