Temporal Variability in Arctic Zooplankton and Phytoplankton Populations from Moored ADCP and Icycler Profiler Measurements
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.An instrumented mooring array across Barrow Strait in the Canadian Arctic Archipelago has provided a 10 year data time series that has been used to determine freshwater and heat transports through this important pathway connecting the Arctic Ocean and the northwest Atlantic Ocean. Measurements indicate that 20% of the total freshwater transport out of the Arctic Ocean passes through this 65 km wide passage, but also demonstrate large seasonal and inter-annual variability in these transports. The timing and duration of a short ice free period in the Strait also varies from year to year, resulting in different water temperature, light and mixing conditions throughout the biological growing season. Here, backscatter data from the acoustic Doppler current profilers (ADCPs) used to provide the transport time series, are analysed to study the behaviour, and temporal variability in the relative biomass of the zooplankton population. ADCP transducer calibrations allow for results from two different years to be compared. Daily profiles of fluorescence collected with a new under-ice moored profiler called Icycler are also presented for the same 2 years. Icycler is an innovative winch system designed to profile the top 50 m once a day, using a sonar to allow a sensor package to come to within 2 m of the ice cover. The yearlong fluorescence records provide a unique look at the nature and timing of phytoplankton activity in the Strait. The backscatter and fluorescence results presented are from 2 years for which the physical environment was notably different. High inter-annual variability in the measured biota are identified. The inter-relationship of the zooplankton and phytoplankton populations and correlations with the physical environment are also explored.
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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 | 0.001 | 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".