Mooring time-series observations of water dynamics in ice-covered Arctic estuarine systems: an analysis of vertical motion cycles/patterns
Bibliographic record
Abstract
In recent decades, the Arctic marine environment has undergone a rapid change as represented by the dramatic reduction in sea ice extent and volume. Despite the recent decline in the Arctic multiyear ice cover, the Arctic and sub-Arctic regions continue to remain seasonally ice-covered. The annual cycle of growth and decay of the seasonal sea ice cover has far-reaching impacts on oceanographic conditions in the Arctic estuarine environments. Due to complicated and costly logistics associated with time-series observations in ice-infested waters, oceanographic moorings proved to be the most reliable and often the only possible method for studying temporal phenomena occurring over the annual cycle in Arctic marine environments. The broadscale goal for this study was to investigate how geophysical oceanographic processes characteristic of the Arctic and sub-Arctic estuarine environments (i.e. presence of seasonal snow and sea ice cover, estuarine and tidal water dynamics, atmospheric forcing and unique under-ice light regime) affect biological processes like zooplankton distribution and DVM throughout long-term mooring deployment. In this Ph.D. thesis, I present field observations relying on mooring observations conducted at three ice-covered locations, each presented in an individual chapter: (1) Young Sound (2014- 2014), which is a fjord in Southeast Greenland, (2) Belcher Islands (2013-2016) in the subarctic Southeastern Hudson Bay, and (3) Western Hudson Bay north of Churchill (2016-2017). From the acquired mooring records, we observed the influence of solar and lunar light and water dynamics on the vertical migration of zooplankton. At locations 1 and 3, we observed modifications or the complete disruption of DVM during highly energetic current and spring tide events. The mooring deployed at location 2 recorded oscillations of temperature and salinity throughout the whole water column, which we attributed to vertical displacement water parcels caused by internal tidal waves. The combination of winter hydrographic data and summer satellite observations of solitary waves confirmed that the observed internal waves were generated at the southeast corner of the Belcher Islands by the interaction of tides with bottom topography. This thesis addressed some of the knowledge gaps existing in the understanding of physical-biological coupling in the Arctic and sub-Arctic estuarine environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".