Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
Bibliographic record
Abstract
This study quantifies morphological features of global braided river deltas using Google Earth imagery, analyzing eight systems (e.g., Yukon–Kuskokwim, Poyang Lake, Lena River deltas). Methods include listwise deletion for missing data (retaining 87% of Poyang Lake delta samples) and sensitivity analysis (threshold changes ≤2.4%). Nonparametric tests (Kruskal–Wallis, H = 12.73, p = 0.005) show significant differences in bifurcation angles across deltas, with the wave-dominated Po River (59.2°) having an 18% higher 80% threshold the than tide-dominated Poyang Lake (50.1°, p = 0.003). Key quantitative results include the following: 1.65% of bifurcation angles cluster at 30–60°, differing from fan deltas (p < 0.01); wavelength–amplitude relationships are nonlinear (R2 = 0.537–0.913), with positive slopes indicating a high sediment supply (e.g., Yukon–Kuskokwim) and negative slope channel avulsion (e.g., Poyang Lake); bifurcation spacing correlates with the sediment supply—54% of Poyang Lake spacings < 2000 m (dense networks) vs. 80% of Lena River spacings < 15,000 m (stable channels). The resulting dataset enables global, remote-sensing-based comparisons, providing thresholds for sedimentary modeling and reservoir prediction. Moderate missing data (≤13%) minimally affect results, though high-missingness cases need further analysis. This study replaces empirical rules with statistical validation, showing that morphometric differences reflect depositional dynamics, which are critical for reservoir heterogeneity assessments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".