Soil seed bank community structure of pastures and hayfields on an organic farm
Bibliographic record
Abstract
Sanderson, M. A., Stout, R., Goslee, S., Gonet, J. and Smith, R. G. 2014. Soil seed bank community structure of pastures and hayfields on an organic farm. Can. J. Plant Sci. 94: 621–631. Understanding the composition of seed banks in pasture soils would help farmers anticipate and manage for weed problems. We characterized the soil seed bank in eight pastures and hayfields [two alfalfa (Medicago sativa L.) and two predominantly grass hayfields; two recently established and two permanent pastures] within an organic dairy farm in southeastern New Hampshire. Seed banks were sampled in the upper 5 cm of soil in each field at a point scale in 2007 and 2010. In 2010, the seed bank was characterized at the field scale by taking soil samples on six 52-m transects in each field. Seed banks sampled at the field scale in 2010 contained 66 plant species. The total number of seeds in the seed bank ranged from 1560 m−2 in grass hayfields in autumn to more than 20 000 m−2 in alfalfa hayfields in summer. Annual forbs dominated the seed bank of alfalfa fields and recently established pastures, whereas perennial graminoids dominated in one grass hayfield and the permanent pastures. These results suggest that management history affects soil seed bank composition and abundance, and these effects should be considered before implementing management practices that could stimulate recruitment from the seed bank.
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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) | 0.001 | 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".