Time-Series Benthic Community Composition and Biomass and Associated Environmental Characteristics in the Chukchi Sea During the RUSALCA 2004–2012 Program
Bibliographic record
Abstract
Benthic macrofaunal and epifaunal composition and biomass and\nassociated environmental drivers were evaluated for time-series stations occupied\nduring three cruises of the RUSALCA (Russian-American Long-term Census of the\nArctic) program undertaken in August 2004, September 2009, and September 2012.\nWe focus on the benthic communities collected at repeat stations in the southern\nChukchi Sea and the key environmental characteristics that could influence benthic\npopulation structure and biomass. These characteristics included bottom water\ntemperature, salinity, and chlorophyll a (chl a); integrated chl a; export production via\nsediment oxygen uptake rates as an indicator of food supply to the benthos; and surface\nsediment parameters that are known to influence benthic population community\ncomposition and biomass, such as grain size, carbon content, and chl a. Overall, both\nthe macrofaunal and epibenthic community composition at the time-series sites in the\nsouthern Chukchi Sea have remained relatively constant over the time period of this\nstudy (2004–2012). However, some of the more sedentary macrofauna are showing\nsignificant declines in biomass since 2004, particularly in the center of a macrobenthic\nhotpot that has been persistent for decades in the southern Chukchi Sea. While\nbiomass estimates were more variable for the more motile epibenthic fauna, there was\nalso an indication of declining epifaunal biomass since 2009. We highlight here as a\ncase study the benthic time-series efforts during RUSALCA that are also part of the\nDistributed Biological Observatory (DBO) international network, which is tracking\nthe status and trends of Arctic ecosystem response to the changing physical drivers in\nthe southern Chukchi Sea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".