Mapping lake water clarity with Landsat images in Wisconsin, U.S.A.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractLandsat thematic mapper (TM) and enhanced thematic mapper plus (ETM+) images are being used to map lake water clarity region-wide in the Upper Midwest states of Minnesota, Wisconsin, and Michigan using a standardized image processing protocol. In Wisconsin, lake clarity estimates have been produced for 8645 lakes in the 1999-2001 time period. In addition to satellite imagery, the protocol relies on Secchi disk data collected by a network of citizen volunteers for development and validation of models. The most significant term in the regression model relating the satellite imagery to the field data is the ratio of spectral radiance values in the blue and red bands (ratio of Landsat band 1 to Landsat band 3). The resulting database of satellite-derived lake water clarity estimates represents an important new resource for lake managers in the region, and for those studying the linkages between lakes and their surrounding landscapes.Les images Landsat-TM et ETM+ sont utilisées pour cartographier la transparence de l'eau de lac à l'échelle régionale dans les états du Minnesota, du Wisconsin et du Michigan, dans le Upper Midwest, à l'aide d'un protocole standard de traitement d'images. Au Wisconsin, des estimations de la transparence de l'eau de lac ont été produites pour 8645 lacs au cours de la période 1999-2001. En plus d'images satellitaires, le protocole repose aussi sur des données de disque Secchi collectées par un réseau de citoyens volontaires pour le développement et la validation des modèles. Le terme le plus significatif dans le modèle de régression reliant les images satellitaires aux données de terrain est le rapport des valeurs de la radiance spectrale dans les bandes du bleu et du rouge (rapport bande 1 / bande 3 de Landsat). La base de données résultante d'estimations de la transparence de l'eau de lac dérivées des données satellitaires représente une nouvelle ressource importante pour les gestionnaires de lacs dans la région et pour ceux qui étudient les liens entre les lacs et les paysages environnants.[Traduit par la Rédaction]
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it