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Record W4415806189 · doi:10.1175/jtech-d-24-0102.1

A Drift-Towing Ocean Profiling (DTOP) System for Monitoring the Ice-Covered Upper Ocean in Polar Regions

2025· article· W4415806189 on OpenAlexaboutno aff
Yongjun Wang, Jinping Zhao, Xiaoyu Wang, Wenli Zhong, Yilin Liu

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSea iceArcticCryosphereArctic ice packIcebergHydrographyIce shelfPolarPolar night

Abstract

fetched live from OpenAlex

Abstract A Drift-Towing Ocean Profiling (DTOP) system has been designed for polar regions to study upper-ocean processes across seasons with the capability for continuous long-term operation. It is comprised of a surface package on an ice floe, a cable for data/power transmission, and a subsurface CTD profiler measuring hydrographic properties to a maximum depth of 125 m. The surface package includes meteorological sensors, an ice temperature chain, and GPS/Iridium antennae. The profiler floats up and down driven by oil bladder expansion and contraction, ensuring safe ice bottom contact for data collection at the ice–water interface. Data collected by the profiler are sent to a shore-based server via an Iridium transmitter. Deployed since 2018 in the Canadian and Eurasian Basins during the Chinese National Arctic Research Expedition (CHINARE) and the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition, DTOPs have gathered 2700 profiles and 48 000 meteorological records by September 2022. Observations reveal seasonal fluctuations in meteorological conditions near the ice surface, sea ice growth/ablation, and water mass characteristics. CTD profiles document polar surface water, near-surface temperature maximum, and Pacific-origin waters, aiding in studying spatiotemporal variations in the mixed layer depth and supercooled water. Future plans involve expanding DTOP deployment with biogeochemical sensors in Arctic basins as part of Arctic Observing Networks. Significance Statement Processes at the ice–water interface in the Arctic are crucial both physically and biologically. Regrettably, conventional underice profilers often start sampling below 10 m to protect their sensors, leaving a significant observational gap just beneath the ice. This study introduces the Drift-Towing Ocean Profiler (DTOP), a new ice-borne drifting air–ice–ocean observing system designed to bridge this gap. This innovative ice-tethered system captures hydrographic and biogeochemical properties throughout the water column from just below the ice, down to a maximum depth of 125 m. By enabling enhanced real-time monitoring, DTOP offers vital insights into the rapidly changing Arctic environment, improving our understanding of ice–ocean interactions and climate dynamics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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