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Record W4280620881 · doi:10.1255/tosf.176

COVID-19: lessons for developing and commissioning new mining technologies

2022· article· en· W4280620881 on OpenAlexaboutno aff
S. Russell

Bibliographic record

VenueTOS forum · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlaggingProject commissioningBusinessConventionCoronavirus disease 2019 (COVID-19)StakeholderService (business)DelicacyPandemicPublic relationsUploadEngineeringMarketingPolitical sciencePublishingGeographyComputer scienceMedicineWorld Wide WebLaw

Abstract

fetched live from OpenAlex

For many of us in the regionally distributed and interconnected mining industries, the pandemic impacts were earlier and broader than most. PDAC, the mining mega-convention that descends on Toronto each spring, started 2020 as per any other year, but by the end of the week the world had changed. Sanitiser bottles appeared on tables, elbow bumps replaced handshakes, and the airports on the trip home were a mix of caution and carnage; a sign of the new reality to which we had now entered. COVID-19 had rapidly spread from being an isolated“Wuhan” virus, and many projects still had field personnel undertaking commissioning and service activities. In the space of a week in March 2020, the focus shifted from urgently completing tasks to evacuating staff back to safety as expeditiously as practicable. Clients were generally supportive of such movements, with similar strategies playing out within their operations. Movements were quickly constrained by pandemic restrictions, and the plane tickets, hotel beds and shipping containers were invariably prioritised for essential operations. Despite high opinions of our indispensability, we ceded priority in most jurisdictions to the public health response. It was only once personnel were back safely in their home cities or in hotel quarantine, stakeholder meetings had been urgently convened across myriad not-yet-ubiquitous online platforms, and formal written correspondence had been exchanged flagging the start of the disruption, that the reality set in; how to continue and complete mining project installations on the opposite side of the continent or world, with operations and suppliers suspended or furloughed, and no certainty as to when personnel and equipment mobility may resume? With very few precedents to draw upon in any of our working careers, the well-intended responses to these disruptions were varied in success, but in any case, will prove formative to how we act in future crises. Whilst we cannot predict with certainty when, where and how the next disaster will occur, it is incumbent on all to take the hard learned lessons of COVID-19 and have disaster response and recovery plans that are updated and reflect our real, lived experiences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.172
GPT teacher head0.468
Teacher spread0.296 · 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.

Study designNot applicable
Domainnot available
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
Published2022
Admission routes1
Has abstractyes

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