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Record W7024628557

Standing Up to Fracking: An evening with 'Slick Water' author Andrew Nikiforuk

2016· other· en· W7024628557 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicRadioactive Decay and Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodParaphernaliaDemotionArticular cartilage damagePretext
DOInot available

Abstract

fetched live from OpenAlex

Investigative journalist Andrew Nikiforuk has been writing about Canada’s oil and gas industry for over 20 years. Now, in his new book Slick Water, Nikiforuk recounts the story of oil patch consultant-turned folk hero Jessica Ernst as she fights to save her land from the damaging effects of hydraulic fracturing.\n\nIn this live event, moderated by The Tyee’s founding editor, David Beers, Nikiforuk will share stories from his extensive reporting on Canada’s energy sector, including a history of the fracking industry and an account of how the industry has broken earthquake records in BC, Alberta and Oklahoma. Nikiforuk will also discuss his new book, Slick Water.\n\nAndrew Nikiforuk has been writing about the oil and gas industry for nearly 20 years and cares deeply about accuracy, government accountability, and cumulative impacts. He has won seven National Magazine Awards for his journalism since 1989 and top honours for investigative writing from the Association of Canadian Journalists.\n\nDavid Beers is the Tyee's founding editor. Under his leadership from  2003 to 2014, The Tyee's traffic grew to eclipse a million page views in a month and its team won many prizes including, twice, Canada's Excellence in Journalism Award, and, twice, the North America-wide Edward R. Murrow Award. He remains committed to the aim that gave rise to The Tyee - pursuing sustainable models for journalism.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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
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
Published2016
Admission routes1
Has abstractyes

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