Short-term morphodynamics and depositional architecture of river-fed turbidite systems in modern and ancient settings.
Bibliographic record
Abstract
Deep-marine environments dominated by submarine avalanches, called turbidity currents, are difficult to study due to their inaccessibility and destructive nature. Linking turbidity current processes to their deposits is particularly challenging in short-time periods (months to centuries) because direct observations of such flows are rare and time resolution of most geological records is limited. Yet understanding short-term morphodynamics is key to improving predictive models. This thesis provides a multidisciplinary analysis using modern and ancient datasets to help bridge the gap between modern flow dynamics and short-term-to-ancient stratigraphic record. The analysis uses repeat-bathymetry maps from months to decades in two modern river-fed systems: Bute Inlet, West Canada, and the Congo Fan, West Africa. Measurements of sediment accumulation, erosion and migration rates over channels, levees and lobe show persistent relative variabilities over different event magnitudes and system settings which quantified the system´s self-organisation (autocyclicity). However, extreme events substantially increase rates of degradation and knickpoint migration and deposition area. Time-lapse maps reveal several-km-long downstream alternations of erosion and deposition, forming Erosion-Deposition Zones (EDZs), where large turbidite macroforms evolve in backstepping and onlap the erosion zones. Extreme events produce EDZs of longer wavelengths and higher amplitudes. EDZs and macroforms are formed by cyclic-step instabilities at the dense-and-fast flow front, while mesoscale bedforms are formed synchronously by cyclic-step instabilities at the flow body. These EDZs short-term morphodynamics create large and thick isolated deposits. An ancient-modern analysis tests the geologic relevance of these new short-term morphodynamics insights using the Miocene turbidites of the Tabernas basin, Spain. The study shows two bedset facies associations pointing to: (A) cyclic-step bedform deposits, and (B) decelerating-flows deposits. Different bedsets combine into turbidite storeys allowing analogies with the Bute Inlet´s macroforms. A sensitivity analysis of sedimentation rates and stratigraphic completeness quantifies the highly fragmented nature, and the high time-space heterogeneities of small, metre-scale deposits in the stratigraphic record.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".