Re-Examining Field-Surveyed Variations in Elevation and Soil Properties with a 1-m Resolution LiDAR-Generated DEM
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it