End‐Member Mixing Analysis as a Tool for the Detection of Major Storms in Lake Sediment Records
Bibliographic record
Abstract
Abstract Major Tropical Cyclone (TC) events cause extensive damage in coastal regions of the western North Atlantic Basin. The short instrumental record leaves significant gaps in understanding long‐term trends in TC recurrence and intensity, creating uncertainty about future storm trends. Analysis of an ∼520‐year core record from Harvey Lake, located >80 km from the Atlantic coast in southwestern New Brunswick, Canada was carried out using: (a) end‐member mixing analysis (EMMA) of lake sediment grain size data to identify storm‐linked sedimentological processes; and (2) ITRAX X‐ray fluorescence (XRF) derived element/ratios (Fe, Ti, Ca/Sr, Zr/Rb, K/Rb, and Br + Cl/Al) associated with precipitation, weathering, catchment runoff, and air masses. Three derived end members were correlated to heavy rainfall events (EM01), spring freshet (EM02), and TCs (EM03). CONISS analysis of the EMMA and XRF core data resulted in recognition of four unique climatic zones distinguished by distinct distributions of TC and rainfall/weathering/runoff/and air masses. Numerous, major (EM01) rainfall events and (EM03) TC events characterized the basal core record during the early Little Ice Age (LIAa; Zone 1) phase, terminating at ∼1645. A near cessation of heavy rainfall and TC events differentiated the subsequent colder LIAb (∼1645–1825; Zone 2) and subsequent Little Ice Age Transition (∼1825–1895; Zone 3). A resurgence of major rainfall and TC events occurred during recovery from the LIA starting in ∼1895 (Zone 4). EMMA provides a robust tool for recognition of TC and major rainfall events, and greatly expands the potential for paleo‐storm activity research well inland from coastal regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".