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Record W4412417416 · doi:10.33002/jpg050101

Flyrock: The “Hits” Just Keep On Coming!

2025· article· en· W4412417416 on OpenAlexaboutno aff
Tony Sevelka

Bibliographic record

VenueJournal of Policy & Governance · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Despite all claims to the contrary, flyrock debris is an inherent and unavoidable byproduct of blasting operations. Wherever blasting occurs, flyrock debris is sure to follow. It is influenced by numerous and unpredictable factors, including blast design limitations, uneven energy distribution, varying atmospheric conditions, human or operational errors, and the inherently dynamic nature of blasting, Additionally, geological inconsistencies, such as variations in rock density, fractures and fault lines, further amplify the unpredictability of flyrock incidents. Flyrock debris poses significant risks to safety, infrastructure, and nearby properties. While blasting has other adverse effects, flyrock is considered the most dangerous due to its potential to injure, permanently disable, or kill on contact. It is responsible for the majority of onsite and offsite injuries and fatalities in blasting operations, many of which could be prevented by enacting mandatory minimum onsite setbacks and mandatory minimum offsite separation distances from sensitive land uses and activities. In Ontario, flyrock remains undefined under the Aggregate Resources Act (ARA), effectively allowing the aggregate industry to avoid regulatory oversight. If flyrock incidents do not result in debris leaving the site or causing injury or death, they remain concealed from the public and unreported to regulating authorities. This lack of mandatory disclosure exacerbates the issue, leaving communities vulnerable to unaddressed hazards. The implementation of mandatory minimum setbacks and separation distances is essential to safeguarding the safety of workers and surrounding communities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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