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Record W2898896318 · doi:10.4043/29132-ms

Usage of Unmanned Aerial Vehicles for Iceberg Surveying and Monitoring - Preliminary Results

2018· article· en· W2898896318 on OpenAlex
Robert Briggs, Carl Thibault, Laurent Mingo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOTC Arctic Technology Conference · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsIcebergGlobal Positioning SystemRemote sensingPhotogrammetryGeologyLidarAerial surveySoftware deploymentRadarMarine engineeringComputer scienceEngineeringSea iceOceanographyAerospace engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Due to their potential instabilities, deploying personnel onto icebergs to make direct in-situ measurement is hazardous. The preliminary results from an investigation into the usage of Unmanned Aerial Vehicles (UAV) for surveying and monitoring icebergs are presented. The project had four objectives: (i) acquisition of imagery for the generation of iceberg topside reconstructions using photogrammetry; (ii) development of a GPS tracking device and a deployment mechanism to place it onto an iceberg; (iii) development of a motion sensor to record the motion of an iceberg and a deployment mechanism to deliver it onto an iceberg; and (iv) iceberg draft measurements from a UAV-mounted ice penetrating radar. The project has used both commercially available and custom-built UAVs. The sensor packages (cameras, tracking devices, accelerometers and ground penetrating radar) were commercial products that have been modified for this study and, when required, mountings and delivery mechanisms have been designed and manufactured to integrate the system together. Fieldwork was performed during the 2017 iceberg season in a near-shore environment (Bonavista, Newfoundland and Labrador, Canada) aboard a survey vessel and, in 2018, from an operational supply vessel offshore Newfoundland and Labrador. The field campaigns were conducted in parallel with an iceberg profiling system that uses an integrated multibeam sonar and LiDAR system to generate composite (topside and subsurface) iceberg reconstructions. These reconstructions can be compared with the results obtained from the photogrammetry and the radar survey. During the 2017 program, iceberg imagery for photogrammetry was acquired and GPS tracking devices were deployed onto icebergs and sea-ice. The longest iceberg track obtained was 21 days. For the 2018 campaign, further photogrammetric data was collected and ground penetrating radar surveys of icebergs were performed. The photogrammetry topside reconstructions and the draft estimates from the ground penetrating radar produced results comparable to measurements from the iceberg profiling system. This project has explored the capability of UAVs to deliver sensor packages onto icebergs, and to take aerial measurements over and around them. They are an emerging technology that, although challenging to work with in the harsh North Atlantic environment, have proved useful.

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.

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 score0.108
Threshold uncertainty score0.344

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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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