Impact of tidal dynamics on diel vertical migration of zooplankton in Hudson Bay
Bibliographic record
Abstract
Abstract. Hudson Bay is a large seasonally ice-covered Canadian inland sea connected to the Arctic Ocean and North Atlantic through Foxe Basin and Hudson Strait. This study investigates zooplankton distribution, dynamics, and factors controlling them during open-water and ice cover periods (from September 2016 to October 2017) in Hudson Bay. A mooring equipped with two acoustic Doppler current profilers (ADCPs) and a sediment trap was deployed in September 2016 in Hudson Bay ∼190 km northeast from the port of Churchill. The backscatter intensity and vertical velocity time series showed a pattern typical for zooplankton diel vertical migration (DVM). The sediment trap collected five zooplankton taxa including two calanoid copepods (Calanus glacialis and Pseudocalanus spp.), a pelagic sea snail (Limacina helicina), a gelatinous arrow worm (Parasagitta elegans), and an amphipod (Themisto libellula). From the acquired acoustic data we observed the interaction of DVM with multiple factors including lunar light, tides, and water and sea ice dynamics. Solar illuminance was the major factor determining migration pattern, but unlike at some other polar and subpolar regions, moonlight had little effect on DVM, while tidal dynamics are important. The presented data constitute the first-ever observed DVM in Hudson Bay during winter and its interaction with the tidal dynamics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".