Field Experiments on Shear Strength of Solid and Freeze-Bonded Sea Ice
Bibliographic record
Abstract
Abstract Sea ice rubble/ridge strength and interaction mechanics are highly important in the design of structures and subsea infrastructure for ice prone offshore environments. To better characterize sea ice conditions in northern Newfoundland, a series of field tests were conducted on landfast ice in Pistolet Bay, NL in February 2018. This paper presents a summary of recent shear strength tests on solid and freeze-bonded ice specimens, to help improve understanding of ice rubble properties and behaviour under field conditions. Both horizontal and vertical sea ice samples were tested under dry and submerged conditions, as well as freeze-bonded ice samples under submerged conditions. Sea ice samples were sheared using the Asymmetric Four Point Bending (AFPB) method, which has been shown to produce a near pure shear region at the center of the specimen. For the dry tests, cores were sheared directly after collection so as to test them in conditions as close to in-situ as possible. For submerged tests, cores were submerged for a specific period of time before shearing. Freeze-bonded samples were prepared using a confinement frame which applied a pressure of 25 kPa to the specimens during submergence. These data for AFPB field tests are an important consideration in modelling the strength of ice rubble/ridges and are the first of their kind. From this work it may be concluded that the AFPB method is a promising approach for studying shear strength of both solid and freeze-bonded specimens in the field and additional testing is recommended. New field testing approaches, such as the one presented here, will help improve understanding of in-situ sea ice properties and behavior, which ultimately supports the development of new ice-structure interaction models, which directly benefits oil and gas, shipping, renewable energy, and public works projects in ice prone Arctic and Sub-Arctic regions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".