Megacollect 2004: Hyperspectral Collection Experiment Over the Waters of the Rochester Embayment
Bibliographic record
Abstract
This work describes the water collection experiment component of the Megacollect 2004 campaign. Megacollect was a collaborative campaign coordinated by RIT with several institutions to spectrally measure various target/background scenarios with airborne sensors and ground instruments. An extension to the terrestrial campaign was an effort to simultaneously measure water optical properties in different bodies of water in the Rochester Embayment. This collection updates a previous effort in which water surface measurements were made during an AVIRIS mission over the Rochester Embayment (May 1999). Megacollect 2004 builds on this through an expanded campaign that increased the number of stations sampled, extended the spectral range of measurements, and improved the spatial resolution of the imagery through the use of multiple sensors (COMPASS, SEBASS, MISI, WASP). A larger set of in-water instruments were deployed on several vessels to sample and measure water optical properties near the shores of Lake Ontario, the northern portions of Irondequoit Bay, and several smaller ponds and bays in the Rochester Embayment. This paper describes the different in-water instruments deployed, the measurements obtained and how they will be used for future modeling efforts and development of hyperspectral algorithms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".