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Record W3115855332 · doi:10.12789/geocanj.2020.47.167

London 2021 GAC–MAC Joint Annual Meeting Field Trips

2020· article· en· W3115855332 on OpenAlexaffvenueabout
Patricia L. Corcoran, P. J. A. McCausland

Bibliographic record

VenueGeoscience Canada · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsTRIPS architectureJoint (building)Computer scienceEngineeringCivil engineeringParallel computing

Abstract

fetched live from OpenAlex

GAC-MAC London 2021 is offering five field trips; four that will run virtually or in person depending on circumstances dictated by the state of the pandemic, and one that will run virtually only.Conference participants will have the option to take part in visiting:1) The deep karstic basin of Crawford Lake on the Niagara Escarpment to examine varves containing light inorganic and dark organic couplets; 2) The geological wonders of the Niagara Escarpment in the Hamilton area, including the sedimentary deposits, fossils, and lateral changes in lithological characteristics that have been affected by continuous erosion; 3) The 1140 to 1105 Ma volcanic and intrusive rocks of the Early Midcontinent Rift from the spectacular Lake Superior shoreline to as far east as Timmins (virtual only), including 'visits' to dykes, interlayered alkaline and tholeiitic basalt, and alkaline rocks of the Coldwell Complex; 4) The well-preserved outcrops of the Paleoproterozoic Huronian Supergroup, including evidence of early life, glacial activity, and effects of the Sudbury meteorite impact; this trip is dedicated to the memory of Grant Young; 5) An informative field trip for earth science educators that will take participants to the Oil, Gas and Salt Resources Library and Hungry Hollow to learn about the paleoenvironment and to create a fossil collection for use in their classrooms, and a tour of the newly-renovated Arkona Lions Museum and Information Centre.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.349
Teacher spread0.310 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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