Past Experience from Arctic Commercial Expeditions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".