Bibliographic record
Abstract
There are few standards or regulations to help stakeholders consider land use and development in the vicinity of existing pipeline systems. Land use planning that considers the existence of pipeline systems can support the planning for and provision of emergency services and pipeline integrity. This approach can also promote public safety and awareness through consistent and collaborative stakeholder engagement early in the land use planning process. In 2016, a CSA workshop was held with a variety of stakeholders impacted by land use planning around pipeline systems. The workshop identified that there was a need for consistency across the jurisdictions in the form of a national standard. The main goal of the new CSA Z663 standard is to provide guidance and best practices for land use planning and development. It also addresses roles, responsibilities and engagement of all stakeholders to help establish a consistent approach to land use planning. A review of CSA Z663 will illustrate how this document provides information, guidance and tools that are inclusive to all stakeholders. This paper will also highlight the history and key drivers behind the new CSA Z663 standard and provide an overview of the current scope and content. Finally, the paper will describe future considerations and additions to the standard.
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.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".