Technical meeting on the traffic light protocols (TLP) for induced seismicity: summary and recommendations
Bibliographic record
Abstract
A technical meeting was held on October 6, 2015, at the downtown campus of the University of Calgary to discuss the effectiveness of the traffic light protocol (TLP) approach for management of risks from induced seismicity. The meeting was attended by 64 participants from industry (55%), various government agencies (25%), academia (10%), and professional societies (5%). The role of TLP in the mitigation of seismic hazards from induced seismicity and its challenges were examined. Three major issues with the current magnitude-based TLPs were identified: (1) possible confusion due to the magnitude uncertainty for an induced seismic event, (2) lack of a link to the impact/consequences of reported seismic event(s), and (3) need to integrate other potential hazard indicators. To improve the effectiveness of existing TLPs, the following changes are recommended: (1) incorporate ground motion information into TLPs such that decisions can be made based on better assessment of the actual risk, (2) develop a standardized approach for earthquake magnitude calculation, and (3) make the TLPs more adaptive to local hazard conditions through research and incorporation (as appropriate) of other hazard indicators. A number of action items were brought forward at the workshop: (1) establishing a uniform standard for seismic data collection and assessment, (2) establishing a coherent framework of data sharing for induced seismicity monitoring and research, (3) sharing other types of data, such as locations of known faults, and (4) taking more proactive approaches to establish best practices and to mitigate seismic risk from induced seismicity. These actions will greatly strengthen the reputation of the hydrocarbon industry with respect to proactive, sensitive and responsible development of unconventional sources. If these steps can be implemented in a timely and effective manner, Canada has the potential to be a world leader in monitoring, understanding and mitigating hazards and risks from injection-induced seismicity.
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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 | 0.062 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.032 | 0.035 |
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".