Sediment-laden sea ice in southern Hudson Bay: Entrainment, transport, and biogeochemical implications
Bibliographic record
Abstract
During a research expedition in Hudson Bay in June 2018, vast areas of thick (>10 m), deformed sediment-laden sea ice were encountered unexpectedly in southern Hudson Bay and presented difficult navigation conditions for the Canadian Coast Guard Ship Amundsen. An aerial survey of one of these floes revealed a maximum ridge height of 4.6 m and an average freeboard of 2.2 m, which corresponds to an estimated total thickness of 18 m, far greater than expected within a seasonal ice cover. Samples of the upper portion of the ice floe revealed that it was isothermal and fresh in areas with sediment present on the surface. Fine-grained sediment and larger rocks were visible on the ice surface, while a pronounced sediment band was observed in an ice core. Initial speculation was that this ice had formed in the highly dynamic Nelson River estuary from freshwater, but δ18O isotopic analysis revealed a marine origin. In southern Hudson Bay, significant tidal forcing promotes both sediment resuspension and new ice formation within a flaw lead, which we speculate promotes the formation of this sediment-laden sea ice. Historic satellite imagery shows that sediment-laden sea ice is typical of southern Hudson Bay, varying in areal extent from 47 to 118 km2 during June. Based on an average sediment particle concentration of 0.1 mg mL–1 in sea ice, an areal extent of 51,924 km2 in June 2018, and an estimated regional end-of-winter ice thickness of 1.5 m, we conservatively estimated that a total sediment load of 7.8 × 106 t, or 150 t km–2, was entrained within sea ice in southern Hudson Bay during winter 2018. As sediments can alter carbon concentrations and light transmission within sea ice, these first observations of this ice type in Hudson Bay imply biogeochemical impacts for the marine system.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".