Lake Name or Name Lake? The etymology of lake nomenclature in the United States
Bibliographic record
Abstract
Summary The rationale for the naming of lakes has often puzzled limnologists. This problem is especially apparent in North America because the nomenclature of lakes across the continent appears to be variable, with ‘Name Lake’ occurring frequently, such as in Trout Lake, but also ‘Lake Name’, as in Lake Sunapee. We examined the potential drivers of lake naming patterns using the U.S. EPA National Lakes Assessment database of c. 1000 lakes chosen in a randomised, stratified design. Potential drivers included major limnological characteristics and geographical position relative to European settlement patterns. Of a list of 814 lakes with this binary nomenclature, almost 20% had a Lake Name, with the other 80% being a Name Lake. Across the U.S.A., lakes with larger surface areas were more likely to have a Lake Name, but there was no significant relationship between nomenclature and maximum depth. Examining naming patterns by EPA ecoregion and by state revealed that Lake Names were more common in the southern states and along the eastern seaboard, regardless of their surface area. Analysis of available databases of lake nomenclature in Europe and Canada suggests that these geographical shifts in lake names may be due to the main European colonist source countries that settled these regions, with Lake Name predominating in countries where Gaelic and Romance linguistic influences were strongest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".