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Record W2557830500 · doi:10.4043/27470-ms

Applications of Iceberg Profiling Data to Improve Iceberg Management Success

2016· article· en· W2557830500 on OpenAlex

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

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsIcebergTowingGeologyMarine engineeringEngineeringOceanographySea ice

Abstract

fetched live from OpenAlex

Abstract Hibernia Management and Development Company Ltd. (HMDC) sponsored a R&D initiative in 2012 with the objective of obtaining high quality three dimensional iceberg profiles off the east coast of Newfoundland and Labrador. The three dimensional iceberg profiles collected during that program have been used in this work to illustrate the potential benefits to iceberg management which could be realized if towing vessels were outfitted with the equipment required to obtain three dimensional iceberg shapes quickly in the field. Tools have been developed under the further financial support of HMDC to assess the stability of the iceberg in order to identify the preferential towing directions to reduce the likelihood of rolling the iceberg during a tow. A tool has also been developed to allow the user to assess the local shape of the iceberg relative to the towing net to help avoid unfavorable iceberg shapes and slopes. The iceberg shapes have also been used to assess the current iceberg towing net design with recommendations for potential improvements to reduce net slippage during a tow.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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