Quantifying primary producer phenology in the Canadian Arctic using submersed oceanographic sensors and satellite remote sensing
Bibliographic record
Abstract
Arctic primary producer phenology is undergoing shifts attributed to the increased transmission of light resulting from climate change-induced declines in sea ice thickness, age, and extent. However, the investigation of phenological events, particularly near the ocean surface, is challenging due to limited time-series observations caused by logistical constraints and accessibility issues. Data collected from a subsurface oceanographic mooring deployed in Dease Strait, Nunavut, from 2017 to 2019 were used to determine primary producer biomass using the normalized difference index (NDI) chlorophyll-a retrieval algorithm. Sentinel-1 and RADARSAT-2 synthetic aperture radar (SAR) data and related meteorological variables were used to identify snow and sea-ice melt phase timing. The results revealed a relationship between light availability and surface primary producer timing and magnitude. Specifically, an 18-day difference in the length of ice algal blooms between 2017 and 2019 was observed, with both blooms peaking near snow melt onset and exhibiting similar daily production rates. The extended duration of the 2019 ice algal bloom was due to lower air temperatures compared to 2017, and a deeper snowpack prior to snow melt. Moreover, a 6-7 day under-ice phytoplankton bloom occurred in both years, coinciding with melt pond formation. However, the 2019 under-ice bloom exhibited a lower accumulation rate, likely due to nutrient depletion beneath the ice by the prolonged ice algal bloom. Following ice break-up in 2019, a 31-day late-summer bloom occurred via sustained wind-driven mixing, highlighting the importance of ocean-atmosphere coupling in the region following ice break-up. The findings of this thesis provide a novel approach to investigating surface primary producer phenology and suggest the potential application of this technique to support future long-term monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".