Natural trait variation for taxonomic classification and breeding potential assessment in the genus Camelina
Bibliographic record
Abstract
Camelina sativa (L.) Crantz or camelina has been the subject of renewed interest as a novel alternative oilseed crop suitable for uses in biofuel, food, and industrial chemical applications as well as sustainable agricultural practices.Camelina's development as an oilseed crop is currently limited by a short breeding history and a complicated allohexaploid genome structure, which hinders traditional breeding and genetic modification approaches.Therefore, our study takes an alternative approach, examining natural (phenotypic) variation in an assortment of I would next like to thank my co-supervisor, Dr. Sara Martin, for providing us with all the necessary resources required at ORDC and her contributions in running flow cytometry to generate the large dataset in this study.Thanks to Dr. Tyler Smith for helping me learn RStudio and accompanying statistical analyses to satisfy my curiosity in using a programming language to efficiently process and organize my dataset.I would also like to thank my M.Sc.Committee Members, Dr. Shelley Hepworth and Dr. Douglas Johnson, for their
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".