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Record W1672342561 · doi:10.1029/gm010p0056

A Reconnaissance Underwater Gravity Survey of Lake Superior

2011· book-chapter· en· W1672342561 on OpenAlexaff
J. Weber, Alan Goodacre

Bibliographic record

VenueGeophysical monograph · 2011
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsDominion Astrophysical Observatory
Fundersnot available
KeywordsGeologyBouguer anomalyCrustGravity anomalyRiftBasaltSeismologyObservatoryGeomorphologyPaleontologyTectonics

Abstract

fetched live from OpenAlex

During September 1964 the Dominion Observatory established 230 underwater gravity stations on a reconnaissance survey of Lake Superior. The Bouguer anomalies, which are presented in the form of an anomaly map, range from −90 to +25 mgal and present a complex pattern in the western part of the lake. Those over the lake may be reasonably explained in terms of surface geology, positive anomalies being produced by middle Keweenawan basalt, and negative by late Keweenawan or Cambrian sediment or Archean granite. The Keweenaw High, a positive anomaly belt between the Porcupine Mountains and Isle Royale, is thought to be an extension of the Midcontinental Gravity High. Although this feature can be explained by near-surface density variations it is also quantitatively interpreted in the light of the results of the Lake Superior seismic experiment, which indicates a thick, high-density crust beneath Lake Superior. The high crustal seismic velocities observed are consistent with the generally positive gravity anomalies over the lake, which indicate the presence of high-density rocks within the crustal column. However, the large variations of crustal thickness determined seismically appear to be localized and due to crustal rifting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.048
GPT teacher head0.215
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2011
Admission routes1
Has abstractyes

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