<b>Observing Lake Michigan Evaporation Influence on Atmospheric Moisture Using Airborne Measurements of Stable Isotopologues in Water Vapor</b>
Bibliographic record
Abstract
The Great Lakes region offers opportunities for shipping and recreation and provides water for consumption to large parts of the United States and Canada. The Lakes themselves significantly influence the downwind states' and provinces' regional weather and climate. Seasonal storms and weather conditions driven by the Great Lakes are challenging to model due to simplifications of the processes involved in evaporation and atmospheric boundary conditions, such as transport, turbulence, and mixing. These processes are also difficult to measure directly, resulting in a lack of ground-truth data for validating models. Additional tracers of evaporation from the lake's surface and resulting boundary layer transport could enhance the understanding of these processes, and stable water vapor isotope tracers could fill that gap.In this thesis, I tested commonly used tubing types to determine if there was a superior material for atmospheric stable water vapor isotope analysis. All of the commonly used materials performed similarly. I also utilized Purdue’s Airborne Laboratory for Atmospheric Research (ALAR) to observe stable water vapor isotopes on a broad atmospheric scale. The lowest hundreds of meters of the atmospheric boundary layer above Lake Michigan showed an increase in water vapor mole fractions and shifts in the isotopic content from the flight observations, indicative of a lake internal boundary layer and lake evaporation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".