Cold-Weather Crop Suitability Modelling
Bibliographic record
Abstract
This article presents ArcGIS Pro workflow results aimed at rating and mapping cold-weather crop suitability from 0% to 100% at 1-m elevation resolution for the Province of New Brunswick (NB). This rating accounts for variations by soil conditions (texture, coarse fragments, depth, calcareousness, drainage, slope), growing degree days (GDD) and frost-free days (FFD) from within fields to across regions. The ratings so produced reflect a significant part of farm and farm/woodlot property assessment values as these also vary by area and building footprint. While the soil properties for texture, coarse fragments, depth, and calcareousness vary by NB soil association mapping units, within-field suitabilities also vary by slope from flat to steep and by drainage as it correlates across the terrain by depth-to-water (DTW) from very poor to poor, imperfect, moderate, well and excessive. Areas marked by 1.5 10% have low to no suitability because of slope-increased soil erosion and trafficability risks. The number of growing-degree and frost-free days across NB were rated to be sufficient for cold weather cropping, except marginally so at the high-elevation locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".