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Record W7125011794 · doi:10.33002/jpg050204

Critical Limitations of the 1980 U.S. Bureau of Mines Siskind et al. Study (RI 8507)

2025· article· W7125011794 on OpenAlexaboutno aff
Tony Sevelka

Bibliographic record

VenueJournal of Policy & Governance · 2025
Typearticle
Language
FieldEngineering
TopicGeotechnical and Mining Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityZoningBenchmark (surveying)Environmental policyCalibration

Abstract

fetched live from OpenAlex

The 1980 study by David E. Siskind et al., “Structure Response and Damage Produced by Ground Vibration from Surface Mine Blasting” (RI 8507), has become a foundational reference for regulatory vibration thresholds across North America and other jurisdictions. Despite its widespread citation, the study suffers from critical limitations in scope, methodology, and applicability — particularly when used as a universal standard in environmental assessments and quarry blast-impact studies, and has the potential to mislead the public and decision makers. This article examines the structural, diagnostic, and planning-related shortcomings of RI 8507, with a focus on its misalignment with Ontario’s diverse receptor types, zoning frameworks, and environmental sensitivities. The analysis reveals that RI 8507 lacks temporal granularity, lacks receptor diversity, lacks diagnostic rigour, and fails to account for modern infrastructure, perceptual impacts, and cumulative exposure over time. These deficiencies undermine its credibility as a universal benchmark and necessitate receptor-specific calibration aligned with contemporary planning and environmental standards.

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 imitation

Not 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.

metaresearch head score (Codex)0.140
metaresearch head score (Gemma)0.233
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1400.233
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.011
Science and technology studies0.0040.006
Scholarly communication0.0040.004
Open science0.0060.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.032
GPT teacher head0.324
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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