Bibliographic record
Abstract
This paper reports an overview of the University of Michigan’s Upper-Great Lakes Observing System (U-GLOS) program, as well as the design, construction, and testing of offshore buoy platforms, communication schemes, and a shorebased server system. Since 2003, the University of Michigan’s Marine Hydrodynamics Laboratories (MHL) has partnered with local communities, as well as Northwestern Michigan’s College Water Studies Institute, DTE, Alliance for Coastal Technologies, Michigan Sea Grant, and the Grand Traverse Band of Ottawa and Chippewa Indians to develop the U-GLOS program that exists today. The U-GLOS program now includes both land and offshore platforms that monitor environmental conditions and report, in real-time, the results to a publicly accessible web site. Each station measures a wide range of properties including air temperature, wind speed, wind gusts, solar radiation, humidity, and more. Buoy stations also measure water temperature (thermistor array), directional and non-directional wave characteristics. Ongoing scientific and engineering research is discussed, as well as an overview of available data products, quality control and quality assurance algorithms, and conformity to the National Data Buoy Center (NDBC) standards.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".