Re-Examining Field-Surveyed Variations in Elevation and Soil Properties with a 1-m Resolution LiDAR-Generated DEM
Bibliographic record
Abstract
This article presents a 2017 LiDAR-DEM guided 1-m resolution examination of field-surveyed elevation and soil property variations (5 × 5 m spacings) conducted in 1977 across a hummocky New Brunswick field used for potato production. This examination revealed that the field incurred minor elevation differences were likely due to upslope erosion, as revealed through increasing Sand % and CF % with increasing elevation, and increasing Silt % along low-lying areas. Soil moisture, field capacity, permanent wilting and nitrate nitrogen (NO3-N) also increased at downslope locations. Directly as well as indirectly, soil pH, ammonium nitrogen (NH4-N), Caesium137 (Cs137) and Mehlich-3 extracted Ca, Mg, K, Fe, Mn, Cu, and Zn were likewise affected by topographic location. Factor analyzing these variables led to: 1) a Soil Loss Factor that captured 24% of the textural variations; 2) a Soil-Cropping Factor accounting for 16% of the N, P, K, Ca, Mg, Mn variations; 3) a Soil Organic Matter (SOM) Factor relating 9% of the in-field variations for SOM, Fe, Zn, Cu to via organo-metal complexation and low NO3-N retention. Many of the topographic variations increased or decreased with the metric DEM-projected depth-to-water index (DTW) index. This index was set to 0 along DEM-derived flow channels with minimum upslope flow-accumulation areas of 0.1, 0.25, 0.5, 1 or 4 ha. Among these, the DTW > 4 ha threshold was useful for reproducing the textural variations, while the DTW > 0.25 ha threshold assisted in capturing trends pertaining to moisture retention and elemental concentrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".