Bibliographic record
Abstract
Dilute lakes, herein defined as having an electrical conductivity ≤25 µS cm−1, are considered valuable culturally, as water and recreational resources; biologically, as unique ecosystems; and scientifically, as sentinels of climate change. However, research and interest has been spread over many disciplines focusing on various issues, making hydrologic comparisons somewhat difficult. Here, 15 dilute lakes are subdivided into lake subtypes based on the dominant hydrologic input and output using annual water balance data. Dilute conditions occur in lakes dominated by all hydrologic inputs (precipitation, surface inflow, and groundwater) and outputs (evaporation, surface outflow, and groundwater), though not all possible input/output combinations produce dilute conditions. Of the nine possible hydrologic lake subtypes (three input by three output fluxes), dilute conditions have been reported in six lake subtypes, not just the traditional seepage and drainage lakes typically suggested in the literature. When annual water balance data are plotted on a “Piper‐type” graph, these six hydrologic lake subtypes are grouped into three hydrologic clusters emphasizing specific hydrologic landscape characteristics and limiting conditions hydrology may impose on naturally dilute lakes. Implications of such a classification system and possible future research questions are discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".