Geostatistical mapping and spatial variability of surficial sediment types on the Beaufort Sea shelf based on grain size data
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it