Recovery of urban Great Lakes beaches after lake-level rise: The roles of infrastructure, sand supply, and management activities
Bibliographic record
Abstract
Managing beaches along urban waterfront corridors of the North American Great Lakes is challenging, as already complex lacustrine hydro-, littoral sand-supply, and coastal morphodynamics are impacted by shoreline and offshore infrastructure in ways not yet fully understood. This paper addresses the legacy controls of geomorphic developments and changes in sand volume within lakefront embayments, during high decadal base water-level conditions, on subsequent beach-recovery dynamics, during lake-level fall. Showcased are insights from annual topobathymetric assessments from 2021 through 2024, over which time Lake Michigan’s base water level fell by ∼ 1 m from its 2020 highstand. Data from ongoing geological monitoring activities were supplemented with federal datasets, which provided information on 2012–2020 sand volumetric changes across urban lakefront embayments with ∼ 1.5 m of lake-level rise. Beach geomorphic developments with 2020–2024 interannual lake-level fall are shown to have been influenced by the legacy of preceding morphodynamic and sand-sequestration patterns. Unlike the shared lake-level and storm histories, these parameters are beach-specific. While all Chicago beach shorelines retreated and experienced overwash into backshore regions during lake-level rise, shoreline advance and foredune re-establishment with lake-level fall have been influenced by preceding sand volumetric changes. This has implications for coastal managers, who must develop site-specific mitigation plans that take intrinsic controls of lakefront structures and time-varying sand-transport patterns on beach morphodynamics into account. The re-establishment of ecologically important foredune areas within urban beaches is of particular interest, given that the urban lakefront of Chicago has recently hosted nesting pairs of the endangered Great Lakes piping plover.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".