Approaching Freshet beneath Landfast Ice in Kugmallit Bay on the Canadian Arctic Shelf: Evidence from Sensor and Ground Truth Data
Bibliographic record
Abstract
The Mackenzie River is the largest river in the North American Arctic. Its huge freshwater and sediment load impacts the Canadian Beaufort Shelf, transporting large quantities of sediment and associated organic carbon into the Arctic Ocean. The majority of this sediment transport occurs during the freshet peak flow season (May to June). Mackenzie River-Arctic Ocean coupling has been widely studied during open water seasons, but has rarely been investigated in shallow water under landfast ice in Kugmallit Bay with field-based surveys, except for those using remote sensing. We observed and measured sedimentation rates (51 g m-2 d-1) and the concentrations of chlorophyll a (mean 2.2 ?g L-1) and suspended particulate matter (8.5 mg L-1) and determined the sediment characteristics during early spring, before the breakup of landfast ice in Kugmallit Bay. We then compared these results with comparable data collected from the same site the previous summer. Comparison of organic quality in seston and trapped material demonstrated substantial seasonal differences. The subtle changes in biological and oceanographic variables beneath landfast ice that we measured using sensors and field sampling techniques suggest the onset of a spring melt occurring hundreds of kilometres farther south in the Mackenzie Basin.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".