Meteorological drivers of interannual variation in transparency of mountain lakes
Bibliographic record
Abstract
In mountain lakes, water transparency is regulated primarily by materials loaded from the surrounding catchment. Consequently, transparency within a lake can vary over time due to meteorological conditions that affect hydrologic inputs. Furthermore, lake responses to these inputs may depend on catchment characteristics. We examined the relationships between meteorological conditions and interannual variation in transparency over thirteen years in a set of four lakes in Yoho National Park, British Columbia, Canada. We measured mid-summer transparency as the attenuation coefficient of 320 nm and collected meteorological data daily with an automatic weather station located near the study lakes. Despite their close proximity, lakes varied in the strength of associations between transparency and cumulative precipitation across a range of durations. Specifically, transparency in non-glacially fed Lake Hungabee was most strongly correlated with cumulative precipitation in the fifteen days preceding sampling, whereas glacially fed Lakes Opabin and Oesa were most strongly affected by longer periods of cumulative precipitation. Consequently, there was relatively little temporal synchrony in interannual transparency variation among lakes. Our results suggest that both the timing and amount of precipitation as well local hydrology must be considered when predicting effects of climate change on mountain lake ecosystems even within a single region.
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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 | 0.001 | 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".