THE INCORPORATION OF AN OCEAN TEMPERATURE PROFILE INTO AN EXISTING ICEBERG DETERIORATION MODEL
Bibliographic record
Abstract
Analytical modelling of iceberg drift forms an essential part of management strategies and scientific investigations. The rate of iceberg deterioration is an important component of iceberg drift models because it affects the life expectancy of the icebergs, and their size, which influences drift speed and direction. Coded models of iceberg deterioration typically use sea surface temperature to characterize the far field temperature of the full water column to which an iceberg becomes exposed. In this work the authors speculate whether the characterization of water temperatures may be improved by considering more closely the ocean temperature profile, both spatially and temporally, but in a manner still conducive to operational modeling. The work is comprised of two parts, the first is an analysis and modelling of monthly water column data available for a discrete location, the second, an example of how the profile “effect” may be used to modify input for an existing deterioration model. The location considered in this study is Fisheries and Oceans Canada (DFO) Station 27, 5 km offshore from St. John’s, Newfoundland. The deterioration model used for the analysis is the Canadian Ice Services (CIS) operational iceberg deterioration model (2007) coded in Matlab. The results (for Station 27) indicate that the modification of water temperature to account for the profile “effect” can result is significant differences in predicted iceberg deterioration rates. In particular the revised input temperature reduces the melting influence of extreme or anomalous sea surface temperature (SST) values for a given month. However, incorporating ocean temperature profile data into the CIS melt model requires considerably more analyses and computational effort. The scarcity of historic ocean temperature profile data in the vast oceanic range of interest is also a considerable stumbling block for routine implementation in operational modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".