Hydrothermal Modeling of Seedling Emergence Timing across Topography and Soil Depth
Bibliographic record
Abstract
The soil environment is an essential determinant of microsite conditions required to model weed seedling emergence timing. An experiment was established across topography within an annually cropped field in south‐central Manitoba to determine the effect of hillslope position (summit, backslope, toeslope), soil residue (native, added) and soil depth (three 25‐mm layers) on the microsite environment and the emergence timing of spring wheat as a surrogate weed. Soil temperature decreased with soil depth whereas soil temperature fluctuation decreased with soil depth and lower hillslope position. Soil water potential was lowest at the summit hillslope position and the upper soil layer. Soil temperature and water potential were combined into hydrothermal time using water potential minimum thresholds (–2.1, –1.3, –0.7, and –0.1 MPa). Thermal accumulation was greatest at the soil surface, whereas hydrothermal accumulation using a water potential minimum threshold of –0.7 or –0.1 MPa was greatest in the 25‐ to 50‐mm and 50‐ to 75‐mm soil depths. Seedling emergence occurred earliest from the 0‐ to 25‐ and 25‐ to 50‐mm soil depths and latest from the soil surface. No differences occurred in seedling emergence timing across levels of hillslope or soil residue. This study identifies recruitment depth as an important microsite variable that influences seedling emergence timing. Weed seedling recruitment models should be based on hydrothermal time and depth of recruitment to reflect the spatial and temporal dynamics of the recruitment zone environment.
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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".