Results of high-resolution technologies applied in the acquisition of seafloor information in the Colombian Caribbean Sea
Bibliographic record
Abstract
We expound on a hydrographic and geophysical survey in the south of the Archipelago of San Andres, Providencia, and Santa Catalina (ASAPSC) in the Colombian Caribbean Sea. In 2017 and 2018, high-resolution data were acquired from the Caribbean Center for Oceanographic and Hydrographic Research (CIOH). The hydrographic data were processed and filtered per International Hydrographic Organization (IHO) standards and the geomagnetic data were processed, corrected, and filtered to improve the interpretation. We could then characterise the submarine relief, analyse the geomagnetic anomalies in the area, and identify different relief forms of volcanic origin. Regarding geomagnetic work, a spectral analysis of the anomalies revealed highly magnetic bodies in deep water and residual magnetic anomalies strongly related to the seabed morphology. Magnetic highs associated with the basement highs and volcanic cones were detected. Complementary spectral analysis showed that the shallowest magnetic sources were in the first 500 m, whereas the deepest magnetic sources were between the depths of 4 and 5 km. The geomagnetic data were also validated using Euler deconvolution analysis, which confirmed these magnetic anomalies. Thus, a direct relationship between the magnetic anomalies and bathymetry of the seafloor was confirmed, reinforcing the theory of the volcanic origin of these islands.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".