Review: Microsite characteristics influencing weed seedling recruitment and implications for recruitment modeling
Bibliographic record
Abstract
Bullied, W. J., Van Acker, R. C. and Bullock, P. R. 2012. Review: Microsite characteristics influencing weed seedling recruitment and implications for recruitment modeling. Can. J. Plant Sci. 92: 627–650. A weed seedling recruitment microsite is the location of a weed seed in the soil profile which affects germination, time of emergence and seedling establishment. The relationship between the recruitment of seedlings and their physical environment, including microclimate, soil, topography, and residue cover can provide the key to understanding the timing of seedling recruitment. The variability that exists in germination and establishment requirements within and among weed species raises important questions for recruitment research addressing multiple species, as well as regional models of genetic variability within species. Current weed recruitment research focuses mainly on summer annual species in annual cropping systems. However, with changes in cropping systems, climate, and weed biology, there will be an increasing demand for the management of both summer and winter annual weeds that develop very early in the spring. Many studies to date take an average of microsite conditions, particularly for soil depth, to describe the seedling recruitment zone. Whereas this practice of under-sampling expedites lengthy and difficult soil environmental measurements, it limits the description of the microsite for predictive purposes. Because soil disturbance disperses seeds to microsites throughout the vertical profile of the shallow seedling recruitment zone, seeds are subjected to gradients of temperature and water that create diverse microsites with depth in the recruitment zone. Research on the interactions of microclimate, field topography, soil properties and agronomic practices can further our knowledge base of the seedling recruitment microsite to better understand weed ecology and population dynamics generally, as well as enhance our ability to predict seedling recruitment specifically.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".