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Record W203629831

Geostatistical mapping and spatial variability of surficial sediment types on the Beaufort Sea shelf based on grain size data

2012· article· en· W203629831 on OpenAlex
Kerstin Jerosch, V E Kostylev, S M Blasco

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsBathymetrySedimentGeologyArcticContinental shelfSea iceOceanographyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

The nearshore Beaufort Sea is a sensitive marine environment that is also the focus of oil and gas exploration. Offshore, the Beaufort Sea contains large potential reserves of hydrocarbons. Any future exploitation of these resources will present unique engineering challenges and will require an understanding of the processes that govern stability, nearshore morphology and sediment properties in the extensive shallow coastal zone of the Beaufort Sea shelf. Knowledge of the surficial sediment distribution is, therefore, necessary to provide a framework for understanding sediment stability, sediment transport, platform foundation conditions and to balance engineering challenges with environmental concerns, resource development and precautionary sustainable management. We describe an approach for a quality controlled mapping of grain sizes and sediment textures for the Beaufort Sea shelf in the Canadian Arctic. The approach is based on grain size data sampled during the period 1969-2008. A replenishment of grain size data since the 1980’s, as well as the consideration of correlating parameters (bathymetry, slope and sediment input) to a cokriging algorithm, amends the former way of mapping the surficial sediments of the Beaufort Sea shelf. 
\nSubsequent to data processing and applying autocorrelation, four single grids (clay, silt, sand and gravel) were generated from grain size data by ordinary kriging and cokriging. Cokriging also considered parameters that influence sediment texture such as bathymetry, slope, cost distance from the Mackenzie River and data anisotropy (directional dependency). The cokriging algorithm expressed as a variogram was quality controlled by cross-validation and predicted standard errors (PSEs). PSE values express a maximum deviation of modeled from the real values and therefore help to estimate the quality in these regions regarding the interpolation results for each grain size range. A sediment type classification scheme applied to the set of clay, silt, sand and gravel content maps resulted in a sediment type map of the Beaufort Sea shelf.
\nThe PSEs of ordinary kriging and cokriging have been compared and showed that the cokriging technique provided superior interpolation results for silt and slightly improved results for clay and sand. Cokriging was able to capture most of the small variations in the sediment texture distribution. Furthermore, reduced nugget effects confirmed that the cost distance grid was a better indicator for sediment texture when compared to bathymetry and slope. For gravel, ordinary kriging achieved better prediction probabilities and was, therefore, used for generation of the distribution map for this grain size class. 
\nThe use of cokriging and ordinary kriging greatly enhanced interpolation estimates without additional sampling. Especially in nearshore regions, like the Beaufort Sea shelf, geostatistical interpolation techniques are very useful for evaluating seabed sediment texture because sampling is often difficult or impossible due to ice conditions or even prohibited near oil platforms. The described methodology along with the inclusion of recent data, provided an improved mapping of the surficial sediments of the Beaufort Sea shelf.

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.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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