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Record W7096400240

DGPS LEVELLING AND MONUMENT STABILITY AT 70 ° NORTH

2015· article· en· W7096400240 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsLevellingPermafrostGlobal Positioning SystemGeodetic datumArcticElevation (ballistics)SubsidenceGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

The International Polar Year and the proposed development of the Mackenzie Delta gas reserves will focus attention on Arctic observation in the coming years. Methods of geodetic observation will be used alongside other scientific experiments to determine the effects of man-made and global warming changes to the environment, which will have impacts on civil infrastructure, and geological and ecological conditions. Changes in elevation are indicative of subsidence due to gas extraction, deepening of the permafrost active layer, the thawing of ground ice, and changes in the eco-system. The challenges of measuring elevation change in such a remote area, where there are few naturally stable areas or artificial benchmarks such as tide gauges, and where observations utilising conventional methods are hampered by the complex hydrol-ogy in the Mackenzie Delta, ensure the pre-eminence of global positioning in this area. Differential GPS (DGPS) levelling has its own unique challenges at 70º North, such as degraded satellite geometry and increased ionospheric effects. In addition, the full error budget of a DGPS method includes the signal of the survey monument in the permafrost environment, which is subject to seasonal heave and settlement. This paper reports on two studies that, combined, indicate the accuracy that can be expected using DGPS levelling onto survey monuments in permafrost at this latitude. The Arctic regions of Canada will be subject to intense study in the coming years due to the planned production of gas in the Mackenzie Delta

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.845

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)0.0000.000

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 head0.098
GPT teacher head0.215
Teacher spread0.117 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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