An Expert System for Using Digital Terrain Models
Bibliographic record
Abstract
functions of various covers. Given the extensive and complex uses to which DTMs can be put, it is of interest to know the impact of errors in the DTMs for these various uses. In ordelr to simplify and codify this knowledge, expert systems can play an important role in advising users on the uses and restrictions for particular DTMs. This paper reports on experiments conducted to investi!gate these errors. We have used elevation, slope, and aspect as features combined with spectral and textural features for Thematic Mapper imagery. DTMs have also been used to generate synthetic images corresponding to the solar illumination angles seen over one day (June 21) and over one year at the satellite imaging time. A mUlti-expert system has been developed to advise users on image analyst. An important component of the Data Preparation Expert is the DTM Expert. This expert system contains knowledge about 13rrors found in DTMs. The reality of these errors is that they vary across the DTM and are functions of the methods used to make the DTM, the topographical relief, the surficiall cover, and the positional location. This reality is modeled simply in the DTM Expert at this time. However, as we gain experience and knowledge of typical DTM errors, the DTM Expert will generate revised accuracy estimates for each point in the DTM. DepEmding upon the use of the DTM, it may be necessary to use the DTM at several different resolutions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.042 |
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".