Bibliographic record
Abstract
Abstract The dates of extreme water levels of two large lakes in northern Quebec have been recorded over the last century by ice scars on shoreline trees and sequences of reaction wood in shore trees tilted by wave erosion. Ice-scar chronologies indicate high water levels in spring, whereas tree-tilting by waves is caused by summer high waters. A major increase in both the amplitude and frequency of ice floods occurred in the 1930s. No such change was indicated by the tree-tilting chronologies, but wave erosion occurred in exceptionally rainy years. According to the modern record, spring lake-level rise is due to increased snowfalls since the 1930s. However, the absence of erosional marks in a large number of years since 1930 suggests a high frequency of low-water-level years resulting from dry conditions. Intercalary years with very large numbers of marked trees (e.g., 1935) indicate that the interannual range of summer lake levels has increased since the 1930s. Increased lake-flood frequency is postulated to be related to a slower expansion of arctic anticyclones, favoring the passage of cyclonic air masses over the area and resulting in abundant snowfall in early winter. Conditions in summer are due to the rate of weakening of the anticyclones controlling the position of the arctic front in summer. This position influences the path of the cyclonic air masses, which control summer precipitation and consequently, summer lake levels in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".