Camelina (<i>Camelina sativa</i>(L.) Crantz) as an alternative oilseed: molecular and ecogeographic analyses
Bibliographic record
Abstract
Camelina (Camelina sativa (L.) Crantz) is an oilseed known for its potential as a low-input biofuel feedstock and its high levels of beneficial fatty acids. We investigated the role of geographical origin in genetic variation and fatty acid content, expecting to find significant variability among 53 accessions and a link between ecogeography and both origin and key oil traits. Amplified fragment length polymorphism (AFLP) fingerprinting revealed high levels of diversity within the 53 accessions. Even though sampling was relatively biased towards the Russian-Ukrainian area, this region was identified as a genetic diversity hotspot and possible centre of origin for camelina. The accessions were categorized by principal coordinate analysis using molecular marker data, enabling identification of links between geographical distribution and these categories. The influence of geographic location on four canola oil quality measures in camelina was evaluated using a geographic information system. These measures were (1) more than 30% alpha-linolenic acid, (2) less than 3% erucic acid, (3) less than 10% saturated fatty acids, and (4) a ratio of alpha-linolenic to linoleic acid greater than 1. The results clearly confirm that camelina oil quality characteristics are strongly influenced by environmental factors. The unprecedented high genetic diversity in this group of accessions offers an excellent opportunity to investigate valuable genes for successful adaptation of camelina to specific ecogeographical conditions such as drought.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".