The Characterization of 10 Spring Camelina Genotypes Grown in Environmental Conditions in North-Eastern Poland
Bibliographic record
Abstract
Camelina (Camelina sativa (L.) Crantz) is an alternative oilseed crop that is garnering increasing popularity due to its multiple applications and greater tolerance to adverse environmental conditions than oilseed rape. The study analyzed selected traits of 10 Canadian and Polish spring camelina genotypes grown in a field experiment in north-eastern Poland in 2015–2018. The greatest differences were observed in seed yield where the effect of weather and environmental conditions explained 72.7% of variance, the effect of genotype explained 5.9% of variance, and the effect of the genotype-by-environment interaction explained 5.7% of total variance. In contrast, 1000-seed weight was not affected by environmental conditions, and it was differentiated only by genotype which explained 73.3% of variance. Genotype was responsible for 4.5%–25.3% of the variance in the remaining traits. The genotype-by-environment interaction explained 2.0%–18.8% of variance in the examined traits. The additive main effects and multiplicative interaction model (AMMI) revealed that genotype 13CS0787-15 was potentially most suited for cultivation in the temperate climate of north-eastern Poland, Central Europe. This genotype was characterized by the highest seed yields and straw yields, as well as the greatest yield stability.
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 | 0.001 |
| 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".