Bibliographic record
Abstract
The timing of spring break-up of landfast sea ice has become less predictable in recent years due to changes in the Arctic climate, which has implications for the traditional lifestyle of Inuit and shipping operations.To study the processes related to landfast ice break-up, meteorological and oceanographic data were collected in Admiralty Inlet, NU in May-June 2019.Numerical experiments using a finite element model (FEM) demonstrated the effects of environmental stresses, ice material properties, and leads on sea ice deformation prior to break-up.Modelled stress magnitudes were well below estimates of tensile yield stress, implying that large-scale ice fracture does not occur under typical conditions.Rather, field data and FEM output suggest the deterioration of ice strength and development of cross-inlet and shore leads preconditioned the ice to allow relatively low wind and current forces to initiate a break-out event in Admiralty Inlet on June 27, 2019. List of Tables 2.1The albedo of typical sea ice surface types (Perovich and Polashenski, 2012).16 4.1 Start and end dates for data collection from the 2019 field season in Admiralty Inlet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39 4.2 Accuracy and resolution of RBR XR620 and Idronaut Ocean Seven 304 CTDs. . . . . . . . . . . . . .
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 | 0.001 |
| 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) | 0.002 | 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".