Delivering Value Added . . . -- Convergence in Geomatics: Challenges and Opportunities
Bibliographic record
Abstract
... digital surface model for 3D visualization, analysis and integration with other remotely sensed data. At Valtus we have been delivering elevation data in various formats for the oil and gas, environmental and infrastructure industries from our extensive online library encompassing Alberta and it surrounding provinces. The elevation data acquired by leading aerial providers has a high degree of accuracy and has been instrumental in our customers ' spatial analysis and visualization needs. We have been using the online delivery mechanism of our spatial data store to allow users to securely search, clip to a given area of interest and download raw datasets (bare and full earth) in various formats and projections. Our customers have then taken these raw datasets and ingested them into different desktop applications for exploitation. However, our customer survey for improvements revealed that even though the datasets were being used in multiple GIS applications, the majority of the users only needed a handful of value added products like shaded relief and contours generated from these raw datasets. This usage trend has created an unnecessary burden on few geospatial experts not only in preparing the data for intake to different applications, but producing and disseminating these products to the non GIS users within their organizations. The major finding of the survey was, will it be possible to stream these value added products directly to the non GIS user’s application instead of going through a multi-step workflow. Valtus has already developed very sophisticated and streamlined terrain processing modules that provide support for automatically creating different kinds of products, which include tiled point clouds, contours, elevation grids, shaded reliefs, intensity/data void images, accuracy analysis and evaluation reports and different metadata products. The
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".