Substrate mapping along a wave-dominated, sand-limited Great Lakes littoral zone: A case study from the bluff coast of Illinois, Lake Michigan
Bibliographic record
Abstract
While detailed geological maps are foundational to aquatic habitat classifications, few efforts have been made to generate these at regional scales along the highly dynamic coastal margins of the North American Great Lakes, where meter-scale fluctuations in water level, storms, and ice-related dynamics induce shoreline and shallow nearshore geomorphic changes. Variances in substrate type, over time, are enhanced in sand-limited settings, where clay-till and other glacial materials, grave-cobble lag deposits, and bedrock outcrops are common. A baseline understanding of sand distributions along these coasts is foundational to ecological and geomorphological inquiries. This paper describes an effort to leverage an offshore geological sample database to map the lake-bottom geology along the bluff coast of Illinois, where littoral sand is scarce and its distribution important to constrain from a coastal management perspective. Offshore geological sample information was integrated with high-resolution federal LiDAR, multi-beam sonar, and backscatter datasets, which provided the means of substrate-unit delineation. Distinction of sand versus non-sandy substrates was reflected in lake-bottom rugosity, backscatter intensity, and sample information. While a sandy lake bottom is smooth, nearshore terrains of greater textural and physiographic heterogeneity relate to craggy bedrock outcrops or a variety of undifferentiated sedimentary deposits, inclusive of gravel-cobble lags and scoured mud-rich till. A tripartite unit division of (1) sand, (2) undifferentiated sediments, and (3) bedrock holds broader application potential to sand-limited nearshore regions of the Great Lakes. Understanding the linkages between lake-bottom geomorphology and geological composition is useful to resiliency planning. Geological monitoring efforts benefit from such regional 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".