Comparison of Lake Ontario and St. Lawrence River hydrologic droughts and their relationship to climate indices
Bibliographic record
Abstract
Five characteristics (intensity or magnitude, duration, frequency, timing, and variability) of drought, defined using the threshold level method (TLM) and recorded in mean annual water levels in Lake Ontario and the St. Lawrence River from 1918 to 2010, were compared. Timing is the only characteristic that is different for the two water bodies. For Lake Ontario, the most intense drought occurred in the 1930s, whereas in the St. Lawrence River, intense droughts took place in the 1960s and 2000s. The Lake Ontario drought produced two shifts in mean before (decrease) and after (increase) the 1930s. The change in variance that took place in the 1960s is thought to be related to the construction of locks during the digging of the seaway. The droughts that affected the St. Lawrence River had no impact on the stationarity (mean and variance) of the annual mean water level series. Analysis of the correlation between drought severity and climate indices revealed that years characterized by very weak to moderate drought are significantly correlated with PDO (Pacific Decadal Oscillation), while those characterized by intense drought are correlated with NAO (North Atlantic Oscillation). Both climate indices are negatively correlated with Lake Ontario water levels, while they are positively correlated with St. Lawrence River levels. The study suggests that NAO may be used to predict the driest years for the two water bodies.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".