MétaCan
Menu
Back to cohort
Record W2894810537

Assessing nearshore sediment and sea surface temperature dynamics using Landsat satellite imagery at Herschel Island, western Canadian Arctic

2018· article· en· W2894810537 on OpenAlexaboutno aff
Konstantin P. Klein, Hugues Lantuit, Birgit Heim, Frank Fell, Jorien E. Vonk, Dirk Jong

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsArcticOceanographySedimentGeologySea iceEnvironmental scienceSatellite imageryPhysical geographyGeographyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

The Arctic is subject to substantial changes due to the greenhouse gas induced climate change. While impacts on lateral transport pathways such as rivers have been extensively studied yet, there is little knowledge about ecological and geological reactions of nearshore environments, even though those are of high importance for native communities. In this study, we use the extensive Landsat archive with comparable data from 1982 on to investigate sediment dispersal and sea surface temperatures under changing seasonal wind conditions in the nearshore zone of Herschel Island in the western Canadian Arctic. Even in the absence of an extensive in-situ dataset, we reveal clear differences between the two prevailing wind conditions (E and NW). During E wind conditions, the Mackenzie River Plume gets distributed over large parts of the Canadian Beaufort Shelf and is the main influencing factor for nearshore sediment dispersal and sea surface temperature dynamics. Contrary, the nearshore dynamics during NW wind conditions are not affected by the Mackenzie River plume, revealing the local nature of the nearshore environment. First field measurements from summer 2017 indicate that recently published SPM and turbidity models are not able to reflect this local nature and strongly underestimate reality. In future, we plan to collect an extensive validation dataset in Arctic nearshore environments to calculate accurate bio-optical models.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 teacher head, not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut)Same topicClimate change and permafrostFrench-language works237,207