Nearshore ice-complex morphodynamics within an urban embayment, Southwestern Lake Michigan: Insights from a winter 2021–2022 monitoring study
Bibliographic record
Abstract
Process-based insights into ice-shoreline morphodynamics are generally lacking, yet are critical for modeling and managing cold climate coastlines. In this study, conducted during the winter of 2021–2022 at an engineered Lake Michigan pocket beach, we address this knowledge gap by integrating repeat footage from beach cameras, aerial photographs acquired during sub-weekly drone flights, weather station information, and precision pre- and post-ice topo-bathymetric survey data to document and categorize the geomorphic evolution of a nearshore-ice complex under varying meteorologic and hydrodynamic conditions. While ice-complex formation and collapse were largely temperature-driven, episodes of sudden expansion followed multi-day wave events, with nearshore wave heights >1.5 m. These conditions promoted the accumulation and integration of brash ice and slush ice, brought in from elsewhere, along the ice front. Rapid nearshore ice-complex (re)expansion, following active erosion of the ice front during the high-energy wave events, was promoted by the calmer marine states to follow, during subzero temperature conditions. The cumulative impacts of shore ice on the lake bottom were captured in pre- and post-ice survey datasets. Up to ∼0.5 m of elevation loss occurred across the nearshore zone of ice advance and retreat, given wave scour along the ice front. Sand losses were roughly balanced by accretion of the lakeward part of the urban embayment. This study offers valuable insights into cross-shore dynamics obscured along more open sections of coast by complex littoral dynamics. Better understanding of ice-shoreline morphodynamics has implications for coastal resiliency planning for anticipated climate change.
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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 | 0.001 | 0.000 |
| 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".