The effects of depth and fluctuating soil moisture on the emergence of eight annual and six perennial plant species
Bibliographic record
Abstract
Weed seedling emergence is partially dependent on biotic and abiotic conditions directly surrounding the seed. When environmental conditions are appropriate, seed germination and emergence occurs. We studied the impact of seeding depth (surface, 1 to 2, 3 to 4, and 6 to 7 cm) and fluctuating soil moisture regimes (field capacity [FC]–1/3FC–FC; FC–1/6FC–FC) on percent weed emergence in a greenhouse. At FC, wild mustard and field pennycress had the greatest percent emergence when seeds were placed on or near the soil surface, whereas percent emergence of barnyardgrass and round-leaved mallow was unaffected by seeding depth. All the perennials tested had the greatest percent emergence at FC when seeds were placed near or on the soil surface, except for common milkweed which only emerged below the soil surface. When soil moisture levels fluctuated, surface seeds of barnyardgrass, catchweed bedstraw, green foxtail, wheat, and wild oat had less emergence than seeds below the soil surface; field pennycress had increased emergence when the seeds were placed on the surface; and round-leaved mallow and wild mustard emergence was unaffected by seeding depth. The emergence of curly dock, dandelion, and perennial sowthistle was unaffected by seeding depth, whereas foxtail barley and quackgrass emergence was reduced when seeds were placed on the surface and soil moisture fluctuated.
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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 | 0.000 | 0.000 |
| 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".