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Record W4299712638 · doi:10.5957/icetech-2010-153

Past Experience from Arctic Commercial Expeditions

2010· article· en· W4299712638 on OpenAlexaboutno aff
Johannes Alme, Ove Tobias Gudmestad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticFishingSubmarine pipelineShipyardThe arcticOceanographySeal (emblem)Marine engineeringGeographyShipbuildingFisheryArchaeologyEngineeringGeology

Abstract

fetched live from OpenAlex

In the past, Norwegian vessels have entered the Arctic for fishing and for hunting whales and seals. The seal hunters needed to go to the iceedge or into the ice to catch the seals and their activity created much needed income in the past. These seal hunters came mainly from the Aalesund area of Norway (many came from the village of Brandal) and from the Tromsø area in the north. Although seal hunting is controversial to day, there might be important learning to bring to new industries like the offshore oil and gas industry and to the navigators in ice infested northern waters. An activity within the research project “PetroArctic” at NTNU has focused on collecting experience data from the seal hunters, (Alme, 2009). A number of interviews with elders (age from 70 to 80+) have been conducted with focus on the physical environmental conditions, vessel behavior in ice and causes of loss of vessels. Among those interviewed were the legendary captain Paul Stark who sailed on sealers from 1950 to 2000 and who was involved in three vessel losses. Newspaper records from the early decades of the 20th century have been reviewed. Prior to the time of steel hull ships with diesel engines, wooden ships with sails and thereafter with steam engines were used. There were frequent losses caused by ice pressure and vessel implosions. Losses were also due to interaction with “ice foots” (Figure1) of multiyear ridges or due to hits from floating ridges on waves. The paper presents characteristic features of vessels used and ice conditions for the different areas where seal hunting took place. These were the Newfoundland area, Labrador coast, Danish Strait, the Area in vicinity of Jan Mayen, North East Greenland coast, Spitzbergen, Eastern Barents Sea towards Novaya Zemlya and the mouth of the White Sea (Figure 2). The causes for the losses or damages to vessels are reviewed in details. In this respect it should be noted that although the ice cap might be shrinking in the future, there will be ice parts of the year over large areas. The ice might even move faster than in the past and get to new areas that traditionally have been ice free. This also relate to the ice of the polar pack that might move more than in the past. There is therefore a strong encouragement to implement the learning of the Arctic pioneers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.034
GPT teacher head0.349
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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