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Record W2738302126 · doi:10.1080/00288306.2017.1346692

Gravity anomaly grids for the New Zealand region

2017· article· en· W2738302126 on OpenAlexfundno aff
J. C. McCubbine, Vaughan Stagpoole, Fabio Caratori Tontini, M. Amos, Euan Smith, Rachelle Winefield

Bibliographic record

VenueNew Zealand Journal of Geology and Geophysics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
FundersVictoria University of WellingtonUniversity of Victoria
KeywordsGeologyGeodesyGravity anomalyFree-air gravity anomalyGeodetic datumGeoidGravimetryDigital elevation modelAnomaly (physics)Bouguer anomalyVertical deflectionCollocation (remote sensing)AltimeterTerrainGeophysicsRemote sensingGeography

Abstract

fetched live from OpenAlex

ABSTRACT We present 1 arc‐minute Bouguer, Faye, free air and topography corrected gravity anomaly grids for the New Zealand region, 25°S to 60°S and 160°E to 170°W. The grids were compiled from existing terrestrial, marine and satellite altimetry‐derived gravity data enhanced with new airborne gravimetry data that were acquired for improvement of the New Zealand vertical datum. The airborne data seamlessly cover onshore and offshore areas over New Zealand's North, South and Stewart islands with a uniform flight line spacing of 10 km. All data were corrected for the gravitational effect of the Geodetic Reference System 1980 (GRS80) reference ellipsoid and tied to the International Gravity Standardization Net 1971 (I.G.S.N.71) gravity datum. The gravity anomaly data from all sources were combined using the method of least squares collocation with a three dimensional logarithmic covariance function. Terrain corrections for gravity anomaly grids were calculated using an 8 m digital elevation model for topography above sea level and a 250 m seafloor topography model.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.563

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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