Bibliographic record
Abstract
Digital Terrain Model (Digital Terrain Model acronym DTM) is arbitrary use of a large number of coordinates in three-dimensional x, y, z coordinates of the point on the ground for a form of Statistics said that the terrain surface morphology is the number of attribute information is a space location characteristics and attributes of the terrain described the figures, initially for the automatic design of highway proposed. With the world of computer technology and the rapid development of 3-D visualization technology into the traditional static two-dimensional map of the three-dimensional terrain modeling makes a Geographic Information System (GIS) and digital mapping, a new field of study. DTM is the basis of geographic information system data, mainly used to describe the ground state of ups and downs, the terrain can be used to extract various parameters such as slope, aspect, roughness, and Visibility analysis, watershed generation applications such as structural analysis. Therefore, the DTM in land use analysis, and rational planning, forecasting flood danger, as well as military navigation and missile guidance systems, and combat electronic sand table, and other fields are widely used. Digital Terrain Elevation Model mainly contains the attributes of surface morphology, as well as other attributes, such as slope, aspect and so on. Terrain Elevation attribute is the basis of the model attributes, other elements of the terrain elevation attributes can be directly or indirectly receive, digital elevation model (Digital Elevation Model, acronym DEM) acts as a digital terrain on the main study. DEM is that the number of regional terrain, elevation Z coordinates on the plane X, Y, the two variables of continuous function of a limited discrete said, a series of ground from the X, Y location and elevation linked by some Organization structure with the actual terrain features that the spatial distribution model, it is a spatial information system an important component part.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".