Community-based nearshore wave and water level monitoring along the Nunavut coast of the Canadian Arctic Archipelago (2021-2023)
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) lies within Inuit Nunangat, the homeland of the Inuit, and encompasses extensive coastal regions of Nunavut where communities depend on shorelines shaped by sea ice, icebergs, permafrost, and oceanographic dynamics during the open-water season. Inuit knowledge provides deep insight into coastal change, but systematic observational data on ocean waves and water levels remain lacking. This limits the ability to model nearshore processes and shoreline response, which are essential for coastal management and adaptation. Here we present a dataset of water levels and wave statistics collected between 2021 and 2023 in partnership with three coastal communities: Ausuittuq (Jones Sound), Canada’s northernmost community; Ikaluktutiak; and Kugluktuk (both in Coronation Gulf). The dataset comprises 19 calibrated pressure sensors moored in the nearshore zone and six deepwater wave buoys deployed in collaboration with Inuit boat operators, capturing over 427 days of hourly observations. The wave climate in this Arctic observational dataset is characterized by maximum significant wave heights ranging from 0.56 to 1.69 m and peak wave periods between 3.3 and 6.0 s. This dataset supports Arctic coastal research, wave model validation, and climate impact assessments in the CAA. All files are published in open formats with structured documentation to ensure transparency, accessibility, and reuse.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".