Features of Providing Engineering and Infrastructure Objects with Geospatial Information
Bibliographic record
Abstract
The urgency of the research is due to the fact that there is a necessity to develop and maintain new mineral deposits for which it is necessary to perform surveying and geodetic works and three-dimensional modeling of the earth's surface. Based on the obtained results the geospatial data are formed. With the help of these data we can design and equip the mineral deposits and determine the geological structures and engineering infrastructure of these deposits. In addition, the geospatial data are the basis and source information of documents during the state cadastral registration and registration of land use rights. On this base, the research has the following scientific and technical task: to analyze the possibilities of using different methods for providing GIS of engineering and infrastructure systems with the geospatial information, and with the data for 3D modeling of the studied objects. Corporate GIS is filled with the data on the state of the engineering infrastructure using the information from space surveying systems with high and medium spatial resolution, as well as survey materials from unmanned aerial vehicles and aerial laser scanning. Monitoring of engineering systems is also carried out using the data of ground geodetic surveys.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".