A time series analysis on the prospective relationships between the water level dynamics of Lake Erie and the El Nino/Southern Oscillation phenomenon.
Bibliographic record
Abstract
In an attempt to gain a better understanding of Lake Erie water level dynamics, this study assesses the relationship between the Lake Erie water balance and the El Nino/Southern Oscillation (ENSO) phenomenon using data collected from 1950--1998. After standardizing the collected data, Box-Jenkins time series techniques were utilized assess the temporal patterns and interrelationships of the Multivariate ENSO Index and the Lake Erie water balance variables. Because the MEI is a relatively new measure of the state of ENSO, little research has been completed using the MEI as air independent regressor variable. As such, the findings in this study merely represent a stepping stone to build from, and should not be considered as definitive. Future studies of the relationship between Great Lakes water levels and the ENSO phenomenon may be best served to incorporate both Southern Oscillation Index (SOI) and MEI, to determine any differences the two indices may have. (Abstract shortened by UMI.)Dept. of Earth Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .J64. Source: Masters Abstracts International, Volume: 39-02, page: 0468. Adviser: P. D. La Valle. Thesis (M.A.)--University of Windsor (Canada), 2000.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".