Ice loading on the Nanisivik wharf over three winters; 2018-2021
Bibliographic record
Abstract
Nanisivik, on Strathcona Sound, was the site of the first deep-water wharf in the Canadian Arctic, coming into operation in 1976. Initial ice investigations were carried out with the instrumentation of the time, providing a good description of ice-structure interaction processes, but less successfully measuring ice loading. In 2017 it was possible to return to the site and install an ice pressure measuring instrumentation system. The system consisted of two ice load panels and a video camera. It successfully ran from September 2017 until January 2021, with 3 full winters of data collected. The system also collected data on air temperature, sea water temperature and tide. Tide and air temperature were the two environmental factors having the strongest relation to ice loading. Local pressures as high as 2.76 MPa on one 0.135 m² zone were measured, although peak pressures on a single zone normally were less than 1 MPa and were not correlated with high pressures in adjacent zones. Local pressure peaks were characterized as being short-term with durations of a few minutes or occasionally a few hours. Ice loading was very intermittent and for short periods. Over the period from January through April maximum force on a 0.135 m² area was 165 kN, 372 kN and 150 kN for the winters of 2018, 2019 and 2020, respectively.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".