Ice island thinning: Rates and model calibration with in situ observations from Baffin Bay, Nunavut
Bibliographic record
Abstract
Abstract. A 130 km2 tabular iceberg calved from Petermann Glacier in northwest Greenland on 5 August 2012. Subsequent fracturing generated many individual large “ice islands”, including “Petermann Ice Island (PII)-A-1-f”, that drifted between Nares Strait and the North Atlantic. Thinning caused by basal and surface ablation increases the likelihood that these ice islands will fracture and disperse further, thereby increasing the risk to marine transport and infrastructure as well as the distribution of freshwater from the polar ice sheets. We use a unique stationary and mobile ice penetrating radar dataset collected over four campaigns to PII-A-1-f to quantify and contextualize ice island surface and basal ablation rates and calibrate a forced convection basal ablation model. The ice island thinned by 4.7 m over 11 months. The majority of thinning (73 %) resulted from basal ablation, but the associated volume loss was ~ 12 times less than that caused by areal reduction (e.g. wave erosion, calving, and fracture). However, localized thinning may have influenced a large fracture event that occurred along a section of ice that was ~ 40 m thinner than the remainder of the ice island. The calibration of the basal ablation model, the first with such field data, supports assigning the theoretically-derived value of 1.2 × 10−5 m2/5 s−1/5 °C−1 to the model's bulk heat transfer coefficient. Overall, this work highlights the value of systematically collecting ice island field data for analyzing deterioration processes, assessing their connections to ice island morphology, and adequately developing models for operational and research purposes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".