Bibliographic record
Abstract
The purpose of the present study was to develop a quantitative methodology to define optimum land use systems. Soil survey data were assessed for capability, suitability and feasibility uses to establish interpretative soil units for which planning and management recommendations could be made. The study was agriculturally oriented due to the availability of agricultural productivity data for suitability assessments from four sources: farmer survey, direct estimate by expert consensus, research station data and plot trials-Soils were grouped using cluster analysis on the basis of permanent inherent soil properties. The technique did not group the soils satisfactorily for management purposes due to statistical limitations of the procedure in assessing overlapping and interdependent variables, such as soil characteristics, and restrictions imposed by the soils data set which was neither adequately large nor diverse to form multimember soil groups. Stepwise discriminant analysis was more successful in assessing the interpretative soils data and in identifying discriminant soil parameters. The Canada Land Inventory derived capability classes were separated by drainage, the quantitatively defined suitability classes were separated by parent material and the socioeconomically defined feasibility groups were separated by pH and coarse fraction. Comparison of the interpretative soils classifications revealed that the capability ratings overestimated actual measured yield and that current land use did not realize the full agricultural potential of the land. Feasibility, unlike capability and suitability, stressed parameters other than soil properties in land evaluation. The suitability assessment based upon actual observed productivity data measured under real market conditions was recommended as the most quantitative land evaluation approach. Other soils can be added to the open ended system and optimal use can be made of all soils using guidelines developed by key farmers under real market conditions for soils suitability groups.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 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".