Spatiotemporal variation of environmental conditions and prey availability that drive Arctic nearshore fish community structure in the Point Barrow, Alaska, region
Bibliographic record
Abstract
The Arctic nearshore surrounding Point Barrow, Alaska, is a dynamic system with complex oceanographic and meteorological processes that drive community composition to change rapidly in space and time. Nearshore fish and zooplankton communities were sampled in the summers of 2013–2015. Spatial, temporal, environmental, and biological drivers of fish community structure in the Arctic nearshore surrounding Point Barrow were investigated using multivariate canonical correspondence analysis (CCA). A CCA model using the 13 most explanatory variables (three environmental, one spatial, four temporal, and five zooplankton abundances) explained 73% of the variance in community structure in this region. Distinct fish communities were identified within the three waterbodies that were studied (Chukchi Sea, Beaufort Sea, and Elson Lagoon), and these distinctions were largely driven by salinity. Species move into the nearshore at various times after landfast ice breaks up, creating an annual succession of species that can be found in these nearshore habitats. Low-latitude species tend to become abundant later in the summer season, whereas true Arctic species are present under the ice or move in shortly after breakup. Arctic species are also more abundant in colder years, whereas low-latitude species dominate during warmer years. The increasing abundance of low-latitude species in the Arctic nearshore may have serious implications for the food webs in these ecosystems as climate change continues.
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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 | 0.001 | 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) | 0.001 | 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".