Bibliographic record
Abstract
Abstract Every operation site, oil and gas drilling, shipping, pipelines and loadingfacilities, need a design basis to be properly planned. Also monitoring of theenvironment when the operation has already started is of utmost importance. This became noticed already in late 1960ies during the Manhattan voyages. Eversince ice data has been collected around various projects both in the westernand eastern Arctic. In Canada the heat was on during the Polar Gas and ArcticPilot Project in the 1970ies and 80ies. The discoveries in the Russian Arcticlaunched systematic arrangements to collect ice data in the in thePechora Sea, Barents Sea, Kara Sea, Ob Bay and offshore Sakhalin Island duringthe last 25 years. This paper describes the main features of typicalarrangements made for a successful data collection expedition, how arrangementsworked and also difficulties met during the execution of such anexpedition. Introduction In a successful project the ice conditions play a significant role and it isof utmost importance to have sufficient data on the design conditions. Forinstance each component in an oil export system like onshore pipelines/terminals, underwater pipelines, offloading terminals, assistant icebreakersand export transportation vessels, has design requirements of its own. A designbasis must be made for each component of the system. This requires aconsiderable amount of work, both data collection in the field and reviews ofliterature as well as analyses to make a reliable design basis. This is thefirst step. Planning of any offshore or transport project requires careful analyses ofsafety, impact and economics of the planned operations and equipment. A lot of information is needed both on the environment as well as of theoperations themselves. Planning the operations and doing the design based oninadequate or unreliable data may lead to uneconomic operation or in worstcases severe damages of the environment or human life.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".