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Record W3019343236 · doi:10.1029/2019jc016007

Observational and Modeling Evidence of Seasonal Trends in Sediment‐Derived Material Inputs to the Chukchi Sea

2020· article· en· W3019343236 on OpenAlexaff
Lauren Kipp, Michael A. Spall, Robert S. Pickart, David Kadko, Willard S. Moore, Jessica S. Dabrowski, Matthew A. Charette

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDalhousie University
FundersNational Science Foundation of Sri LankaNational Oceanic and Atmospheric Administration
KeywordsBenthic zoneOceanographyHaloclineSedimentEnvironmental scienceShoalPhytodetritusWater massColored dissolved organic matterGeologyArcticNutrientHydrology (agriculture)PhytoplanktonEcologySalinityGeomorphology

Abstract

fetched live from OpenAlex

Abstract Benthic inputs of nutrients help support primary production in the Chukchi Sea and produce nutrient‐rich water masses that ventilate the halocline of the western Arctic Ocean. However, the complex biological and redox cycling of nutrients and trace metals make it difficult to directly monitor their benthic fluxes. In this study, we use radium‐228, which is a soluble radionuclide produced in sediments, and a numerical model of an inert, generic sediment‐derived tracer to study variability in sediment inputs to the Chukchi Sea. The 228Ra observations and modeling results are in general agreement and provide evidence of strong benthic inputs to the southern Chukchi Sea during the winter, while the northern shelf receives higher concentrations of sediment‐sourced materials in the spring and summer due to continued sediment‐water exchange as the water mass traverses the shelf. The highest tracer concentrations are observed near the shelfbreak and southeast of Hanna Shoal, a region known for high biological productivity and enhanced benthic biomass.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.127
GPT teacher head0.341
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2020
Admission routes1
Has abstractyes

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