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Record W2581664766 · doi:10.1007/s13280-016-0894-2

Sea ice and primary production proxies in surface sediments from a High Arctic Greenland fjord: Spatial distribution and implications for palaeoenvironmental studies

2017· article· en· W2581664766 on OpenAlexaff
Sofia Ribeiro, Mikael K. Sejr, Audrey Limoges, Maija Heikkilä, Thorbjørn Joest Andersen, Petra Tallberg, Kaarina Weckström, Katrine Husum, Matthias Forwick, Tage Dalsgaard, Guillaume Massé, Marit‐Solveig Seidenkrantz, Søren Rysgaard

Bibliographic record

VenueAMBIO · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of ManitobaUniversité Laval
FundersNationale Geologiske Undersøgelser for Danmark og GrønlandPinngortitaleriffikVillum FondenDanmarks Frie ForskningsfondMinistry of EnvironmentMiljø- og FødevareministerietAarhus Universitet
KeywordsFjordGeologyOceanographyArcticSea iceArctic ice packGroenlandiaSpatial distributionThe arcticPrimary (astronomy)Physical geographyIce sheetGeographyRemote sensing

Abstract

fetched live from OpenAlex

In order to establish a baseline for proxy-based reconstructions for the Young Sound–Tyrolerfjord system (Northeast Greenland), we analysed the spatial distribution of primary production and sea ice proxies in surface sediments from the fjord, against monitoring data from the Greenland Ecosystem Monitoring Programme. Clear spatial gradients in organic carbon and biogenic silica contents reflected marine influence, nutrient availability and river-induced turbidity, in good agreement with in situ measurements. The sea ice proxy IP25 was detected at all sites but at low concentrations, indicating that IP25 records from fjords need to be carefully considered and not directly compared to marine settings. The sea ice-associated biomarker HBI III revealed an open-water signature, with highest concentrations near the mid-July ice edge. This proxy evaluation is an important step towards reliable palaeoenvironmental reconstructions that will, ultimately, contribute to better predictions for this High Arctic ecosystem in a warming climate.

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)0.000
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.269
Teacher spread0.243 · 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

Citations60
Published2017
Admission routes1
Has abstractyes

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Same venueAMBIOSame topicGeology and Paleoclimatology ResearchFrench-language works237,207