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Record W4402622389 · doi:10.29150/jhrs.v13i7.261399

Tailing dams’ accidents and compliance failures: A study in Brazil and Canada

2024· article· en· W4402622389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Hyperspectral Remote Sensing · 2024
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)Forensic engineeringEnvironmental sciencePsychologyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

The mining is an activity of importance to the world economy moving billions of dollars/years and employing a network of people. After the tragedies observed in Brazil over mining dams, reflections have been raised about the safety of these environments and their impacts on the environment. This paper focused on compliance weakness on tailing dams' regulations in Brazil and the impacts of environmental accidents in Canada. Besides that, it did a bibliometric analysis with tailing dams’ strings/terms relationed and a comparative Brazil-Canada, a developed country where a similar accident with a tailing dam happened. It discussed the following topics: Salient keywords and Emerging themes, temporal and geographical distribution of publications; growth publication; word cloud, the federal legislation, and instruments for safety of dams, regulations, and the role of inspections agencies, tailing dam accidents and the Canadian scenario of mining regulations and make a comparison between these two countries.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.968

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)0.0000.000

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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