Bibliographic record
Abstract
This dissertation focuses on sea ice observations in Nares Strait between 2003 and 2012. Ice transported via the channel contributes to freshwater flux through the Canadian Arctic Archipelago (CAA). Nares Strait, which forms the eastern boundary of the CAA, is second only to Fram Strait for volume outflow from the Arctic basin. Flow through the channel is described by two regimes. The distinction between these is the presence or absence of a land-fast ice bridge blocking ice transport. During the beginning and end of this study, ice bridges form each year and last for 3 to 6 months. However, ice flows virtually uninhibited for four years beginning in 2006. Data gathered from ice profiling sonars (IPS) moored in the channel are used to measure ice draft. Ice is found to be thicker in the western channel and to have highest velocity in the central channel. The statistical distribution of ice is assessed at seasonal and inter-annual scales temporally. Whereas ice in the Arctic Basin has been thinning, thick multi-year ice continues to flow through Nares Strait. With a goal to estimate ice volume flux through the channel, use of a steady state semianalytic channel flow model to supplement spatial and temporal gaps in ice velocity data is evaluated. Specifically, its ability to reproduce geostrophic flow characteristics and surface velocities in a cross-section of Nares Strait is assessed by comparison to well-resolved observational data. Surface forcing due to winds, the presence of mobile ice and land-fast ice cover conditions are implemented in the model. In order to replicate ice velocity at the water surface, the model requires extreme values for viscosity and amplified drag coefficients. A time series of ice flux is finally derived. Annual ice volume transport through Nares Strait averages 171±62 km3 when an ice bridge blocks the channel as compared to 472±126 km3 when ice flows freely year-round. Thus, Nares Strait transports between 6 and 21% of the volume of ice transported by Fram Strait.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".