Abundance, biomass and community structure of epipelagic zooplankton in the Canada Basin
Bibliographic record
Abstract
Changing environmental conditions such as decreasing sea ice cover impact Arctic zooplankton.In the Canada Basin, zooplankton surveys have seldom been done due to its traditionally thick, year-round ice cover.Here, we describe the zooplankton community of the Canada Basin before the two recent sea ice minima (2007 and 2012).Zooplankton were sampled from the upper 100 m during August and September of the years 2003-2006 using a 150-μm mesh net to determine species composition, abundance and biomass.To describe the zooplankton community and its relation to the environment, we used Bray-Curtis similarity, and then applied hierarchical clustering, non-parametric multidimensional scaling and the BEST BIO-ENV routine.The most abundant zooplankton species in all years were smaller copepods such as Oithona similis and Microcalanus pygmaeus.Biomass was dominated by larger copepod species such as Calanus hyperboreus and Calanus glacialis.For the non-copepod zooplankton, the pteropod Limacina helicina and the larvacean Fritillaria borealis were typically the most abundant species.The non-copepod biomass was dominated by the chaetognath Eukrohnia hamata and L. helicina, while F. borealis contributed relatively little to the overall biomass despite its high numbers.Zooplankton communities differed between shelf/slope and basin stations.We found no obvious interannual changes in community structure over our short 4-year observation period, with community structure influenced to a small degree by environmental factors.
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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 | 0.002 | 0.001 |
| 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".