Mapping Beaufort Sea Topography and Geophysical Settings Using High-Resolution Geospatial Data and GMT
Bibliographic record
Abstract
The papers presents an integrated processing of the high-resolution thematic data covering the area of the Beaufort Sea, a marginal sea of the Arctic Ocean, northern Canada and Alaska. Five thematic maps of the Beaufort Sea, Arctic Ocean are presented. The cartographic techniques were performed by Generic Mapping Tools (GMT) scripting toolset. The methodology presents the integration of the multi-source high-resolution thematic datasets: bathymetric GEBCO, IBCAO, topographic GLOBE, sediment thickness GlobSed, EGM2008 geoid model, GMT vector layers and geophysical gravity model from CryoSat-2 and Jason-1. There is an agreement with the data by their inspection and analysis of grids correlation. The bathymetric map demonstrated variations in depths with rapidly decreasing values in the Mackenzie River coasts, depicting the basin of the Beaufort Sea, large shelf in the Canadian Arctic Archipelago and western part bordering the Chukchi Sea. The GDAL inspection shows that the GEBCO-based topography ranges between -3,973 m to 2,578 m. Gravity data shows that coastal areas in northern Canada and Alaska have values >20 mGal while the basin of the Beaufort Sea is dominated by the lower values at -65 to -45 mGal; the data range is from -155.097 to 366.939 mGal. The marine free-air gravity fields and geoid data demonstrate correlation with topographic isolines of the region. The data range for the sediment thickness is from 0.00 to 18064.53 m having maximal data at the Mackenzie River discharge area. A comprehensive compilation of the data on the Beaufort Sea visualized using GMT presents more insights into its bathymetric structure and geophysical fields distribution in context of the variability of the geological settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".