Characteristics of Black Medic Seed Dormancy Loss in Western Canada
Bibliographic record
Abstract
Core Ideas Self‐regenerating legumes reduce the cost of cover crop seeding. Regeneratfion of black medic from seedbank mediated by temperature. Extreme cold soil temperature may limit black medic regeneration. Successful use of black medic (Medicago lupulina L.) as a self‐regenerating cover crop requires a better understanding of its seed dormancy loss in the field. A two‐stage temperature mediated process for physical seed dormancy loss has been established for black medic in temperate regions, but the temperatures required to precondition seeds for germination (Stage 1 softening) remain unclear. Black medic seeds were placed in fields (October 2003) at three western Canadian sites (Winnipeg, MB; Indian Head, SK; and Lethbridge, AB) and sampled at regular intervals from November 2003 to August 2004. Stage 1 softening (called softening) in retrieved seeds was determined by exposing seeds to a 6/15°C diurnal fluctuating temperature regime. Softening was detected after 3 to 4 mo in Winnipeg and Lethbridge, respectively, but not at Indian Head. Burying seed (2‐cm soil depth) resulted in some greater softening than seed on the soil surface, especially in winter. A population of cultivar George black medic subjected to 11 yr of no‐till farming showed similar levels of softening to the original cultivar George. Seed production location also had only minor influences on seed softening. It was speculated that the lack of seed softening at Indian Head was due to extreme soil temperature (–10 to –15°C) exposure. A controlled environment study confirmed that extreme low temperature exposure (–23°C) did indeed result in low and inconsistent seed softening compared with 5°C and –5°C temperature regimes. Information from this study will assist in management of black medic as a self‐regenerating cover crop.
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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 | 0.001 | 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) | 0.001 | 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".