CSA S250-11 and CI/ASCE 38-02—How to Effectively Utilize These Utility Standards for Shale Energy Projects
Bibliographic record
Abstract
Published in September 2011, the CSA S250 — Mapping Underground Utility Infrastructure — will play a key role in the creation of more accurate and reliable as-built utility drawings and maps. CI/ASCE 38-02 - Standard Guidelines for the Collection and Depiction of Existing Subsurface Utility Data, which was published in 2003, has already been used extensively on infrastructure projects across Canada and the US. Together, these two standards form a solid foundation to both map and records utility infrastructures. The location of existing utilities can play a key role in the design and implementation of infrastructure projects associated with Shale Oil & Gas production. These impacts need to be managed properly to avoid significant cost and schedule ove runs. CI/ASCE 38-02 forms the basis for engineers to create accurate drawings of existing conditions for the project. The data proved up front is critical; however, equally important is the generation of accurate, reliable maps and drawings of new utility infrastructure placed in the ground. Creation of these new records is the primary focus of the CSA S250 Standard. The paper will review the key highlights of the new CSA S250 standard, including - standard symbology, Accuracy Levels, and data recordkeeping - and how it will improve our future knowledge of underground infrastructure. It will highlight the government and private agencies - such as the Ontario Ministry of Transportation, City of Toronto, and Fortis Gas - currently using or looking to implement the use of the standard, and identify how the knowledge learned from that exercise can assist the Shale Energy Industry. Accurate, reliable drawings of our underground utility infrastructure produced in accordance with these industry standards is a benefit to all stakeholders - Project Owners, Designers, Utilities, Regulators and Contractors. Further promotion for the use of these standards is the key if we wish to raise the bar and move forward as an industry.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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".