Lessons Learned From Supporting a Geohazard Management Program
Bibliographic record
Abstract
There are a number of geomatics tasks required to support a Geohazard Management Program (Program). For the program implemented by BGC Engineering Inc. for several midstream pipeline operators, these tasks range from identification of potential geohazards (landslide, river erosion), to setup and support for field navigation, through to geohazard database management. Doing these in an efficient and effective manner requires substantial amounts of spatial data and a toolset containing both software and hardware components. For this Program geohazards are classified as hydrotechnical (e.g. a pipeline crossing a river) or geotechnical (e.g. a pipeline traversing a slope). Lists of potential geohazards are generated and provided to field crews who then navigate to each site and perform a field inspection. Navigation and inspection observations are accomplished with the aid of a ruggedized laptop connected to wireless GPS. Upon return from the field, sites are uploaded to Cambio™, an internet database for managing geohazards. Each site is assigned a frequency of action commensurate with the estimated level of risk. Assigned actions include follow-up ground inspections, detailed investigations, monitoring, maintenance and mitigation. An audit trail of site inspections, surveys and mitigation reports, photos, and site survey drawings, are all available for review within Cambio™, allowing access to the information from any site with an internet connection. This paper will present an overview of the Geohazard Management Program from a geomatics perspective, highlighting the integration of geomatics tools into a system designed to be used by engineering personnel, field technicians, and project managers.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".