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Record W2563986245 · doi:10.12789/geocanj.2016.43.110

Kingston 2017: GAC–MAC Joint Annual Meeting Field Trips

2016· article· en· W2563986245 on OpenAlexaffvenueabout
Dawn A. Kellett, Laurent Godin

Bibliographic record

VenueGeoscience Canada · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeotourism and Geoheritage Conservation
Canadian institutionsQueen's UniversityGeological Survey of Canada
Fundersnot available
KeywordsTRIPS architectureJoint (building)Field (mathematics)Environmental scienceEngineeringTransport engineeringMathematicsCivil engineering

Abstract

fetched live from OpenAlex

the 2017 Annual meeting of the GAC-MAC. The meeting will coincide with the 175 th anniversary of the founding of the Geological Survey of Canada, which was established by the legislature of the Province of Canada in 1842, in Kingston, and with Canada's 150 th anniversary celebrations. The local geology surrounding Kingston, commonly called the Limestone City, does not disappoint and multiple field trips associated with the meeting will take advantage of its unique location. Kingston is located at the eastern end of Lake Ontario, where the St. Lawrence River begins, draining the waters of the Great Lakes into the Gulf of St. Lawrence. The transition from lake to river occurs east of Kingston Harbour, where the nearly flat-lying Early Paleozoic limestone, rimming the eastern Lake Ontario basin, border against a NW-SE trending, low ridge of Grenvillian Precambrian basement rocks, locally known as the Frontenac Arch, which connects the southeastern Ontario part of the Canadian Shield with the Adirondack Massif of northern New York State. The crystalline basement rocks form a resistant ridge over which the St. Lawrence River flows northeastward from Lake Ontario, creating the 'Thousand Islands,' a well-known tourist and cottage region along the international border that now also includes a National Park.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.999

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 head0.014
GPT teacher head0.180
Teacher spread0.166 · 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 designObservational
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
Published2016
Admission routes3
Has abstractyes

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