Intra-specific and inter-sub-specific crossing in lentil (<i>Lens culinaris</i> Medik.)
Bibliographic record
Abstract
Lentil crosses (Lens culinaris ssp. culinaris and L. c. ssp. orientalis) were carried out in the greenhouse and in the field, and the effects of genotype and some environmental conditions on crossing success were assessed. In the greenhouse in the fall, the independent variables “Male” and “Hour” influenced pod and seed set per pollinated flower and “Temperature” affected seed number per pod. Some genotypes and different cross combinations were better under greenhouse conditions. Seed set per pollinated flower of the best inter-sub-specific cross combination in the greenhouse (Lupa × orientalis) averaged 55.2%, while for other intra and inter-sub-specific crosses the average ranged between 3.6 to 23.5%, with an average seed set of 24.4%. Selfed progeny, as determined by morphological and molecular markers, was 5%. Fall crossing success in the greenhouse was favored by temperatures of 20–25°C and sunny mornings. In the field, none of the dependent variables significantly influenced pod or seed set per cross. The intra-specific field seed set per pollinated flower ranged from 0 to 31.1%, with a mean seed set of 13.3%. Spring crossing success in the field was favored by cloudy and rainy days with mild temperatures. Under field conditions, intra-specific crossing success was considerably lower in the spring compared to the inter-sub-specific (L. c. ssp. culinaris × L. c. ssp. orientalis) success obtained in the greenhouse in the fall. Key words: Lentil, Lens culinaris ssp. culinaris, Lens culinaris ssp. orientalis, crossing success, hybrids, environmental conditions
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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 | 0.001 | 0.001 |
| 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".