Seasonal and interannual variability of the Queen Maud Gulf ecosystem derived from sediment trap measurements
Bibliographic record
Abstract
Abstract Several years of monitoring in the northern Canadian Arctic Archipelago contrast with a lack of repeated measurements in Queen Maud Gulf. As sea ice cover declines and maritime traffic increases along the Northwest Passage, Queen Maud Gulf, including the protected national historic sites of the HMS Erebus and HMS Terror wrecks, may experience drastic environmental changes. As part of the Kitikmeot Marine Ecosystems Study, moored oceanographic devices including a sediment trap were deployed in Queen Maud Gulf over three consecutive annual cycles from October 2015 to August 2018 to study the functioning of this geographic bottleneck along the Northwest Passage. Seasonal and interannual variability in total particulate matter (TPM), particulate organic carbon (POC), and microalgal fluxes were investigated in relation to wind speed, snow depth, sea ice cover, water temperature, and current velocity. Zooplankton and meroplankton present in the sediment trap were enumerated to monitor their composition and seasonal development. Landfast ice breakup consistently occurred in early June, followed by sea ice breakup in July. The release of sea ice algae was initiated in March 2016, May 2017, and June 2018, whereas peaks in diatom fluxes occurred in August or September. Sustained abundance of pelagic copepods and meroplankton suggested an ecosystem sufficiently productive to support the year‐round development of the pelagic and benthic communities. Elevated autumn TPM and POC fluxes reflected wind‐induced mixing and resuspension in the absence of ice cover. Our results provide a baseline for evaluating the impact of environmental changes on the Queen Maud Gulf marine ecosystem.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".