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Record W2359907897

Research on Algorithm of Generating 3D Terrain from Contour Map Based on Auxiliary Line

2011· article· en· W2359907897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJisuanji fangzhen · 2011
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsContour lineElevation (ballistics)TerrainCorrectnessPixelLine (geometry)Artificial intelligenceComputer scienceDigital elevation modelComputer visionTopographic map (neuroanatomy)AlgorithmMathematicsRemote sensingGeologyGeometryGeographyCartography
DOInot available

Abstract

fetched live from OpenAlex

Aiming at the problems in automatically sampling data from contour map and automatically assigning the terrain elevation,the data-sampling algorithm based on auxiliary line from contour map was designed,in which the data was automatically sampled,the terrain elevation automatically was assigned.The elevation was computed and assigned automatically according to the peak elevation,contour interval,and the elevation of any contour line.Based on the characters of isolation and continuity of pixels,the pixel points of contours were tracked,and the contour was sampled by some rules.At last,the characters of this algorithm were summed up,and its correctness and validity were proved by the simulation experiment.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.484
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.351
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it