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The Experience of Using Autonomous Drifters for Studying the Ice Fields and the Ocean Upper Layer in the Arctic

2017· article· en· W4251575420 on OpenAlexaboutno aff
S.V. Motyzhev, E.G. Lunev, A.P. Tolstosheev

Bibliographic record

VenuePhysical Oceanography · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBuoyDrifterSea iceArcticEnvironmental scienceArctic ice packOceanographyGeologyPolar nightClimatologyMeteorologyGeography

Abstract

fetched live from OpenAlex

The constructional and operational features of the BTC60/GPS/ice temperature-profiling drifters, developed in Marine Hydrophysical institute RAS for investigation of polar areas, are considered in this article. The drifters operated in completely automatic mode measuring air pressure, water temperatures at 17 depths down to 60 m, ocean pressures at 20, 40 and 60 m nominal depths and current locations. Accuracies of measurements were: +/-2 hPa for air pressure, +/-0.1C for temperatures, +/-30 hPa for ocean pressure, 60 m for locations. Iridium satellite communication system was used for data transfer. Time delay between sample and delivery to a user did not exceed 10 minutes. More than 30 thermodrifters were developed in the Beaufort Sea -Canada Basin and central Arctic for the period from September 2012 to September 2014. Total duration of drifting buoys in operation was more of 4800 days. It was accepted the data of hourly samples about variability of ice-flows and ice field as a whole movements, thermo processes within upper water layer below ice, air pressure in near surface atmosphere of the Arctic region. The article includes some results of statistical analysis of data from drifter ID247950, the 3-year trajectory of which depended on the processes of transfer and evolution of ice fields in the Beaufort Sea -Canada Basin. Over a long period of time the Arctic buoy in-situ experiments allowed resulting about capability and reasonability to create reliable, technological and low-cost buoy network on basis of BTC60/GPS/ice drifters to monitor Arctic area of the World Ocean.

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)0.000
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 score0.195
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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