Extracting topographic characteristics of landforms typical of Canadian agricultural landscapes for agri-environmental modeling. I. Methodology
Bibliographic record
Abstract
Li, S., Lobb, D. A., McConkey, B. G., MacMillan, R. A., Moulin, A. and Fraser, W. R. 2011. Extracting topographic characteristics of landforms typical of Canadian agricultural landscapes for agri-environmental modeling. I. Methodology. Can. J. Soil Sci. 91: 251-266. Soil and topographic information are key inputs for many agri-environmental models and there are linkages between soil and topography at the field scale. A major source of soil data is soil databases established based on field soil survey. Although both soil and topographic information are recorded in field soil surveys, the nominal nature of the topographic data has limited their use in agri-environmental models. In this study, we developed a methodology to extract various topographic derivatives and to classify the landscape into landform elements with distinctive topographic characteristics based on detailed analyses of fine resolution digital elevation models. Data obtained from these analyses were used to calculate a representative two-dimensional hillslope of five segments, each with a defined length and slope gradient. A set of modal hillslopes was developed to describe topographic variability. Additional topographic parameters, ratios and indices were calculated to reflect different aspects of topographic characteristics and also to build connections between different agri-environmental models. In particular, a topographic complexity index was developed as a quantitative measure of the degrees of divergence and convergence. This paper describes the methodology using one site as an example. Application of this methodology to other landforms in agricultural land of Canada is reported in a companion paper.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".