SATELLITES: Students and Teachers Exploring Local Landscapes to Interpret the Earth from Space
Bibliographic record
Abstract
SATELLITES program is designed to introduce in-service teachers and K-12 students to basic geographic concepts, geospatial technology (e.g., remote sensing, GIS, GPS, and digital elevation modeling), related data, and applications in complex concepts in Earth System Science. Teachers, who received extensive SATELLITES training in a one-week summer institute in 2006, integrated concepts and technologies in their school curriculum the following fall by engaging students in inquiry-based research projects during an intensive field campaign. 151 K-12 teachers from 110 schools have received SATELLITES training over the last three years and over 10,000 students representing more than 300 schools from every state in the United States and several other countries including Canada, Australia, Great Britain and China have participated in data collection during field campaigns. In 2006, 1200 student observations were recorded and 600 students attended the 2006 annual conference where 60 inquiry-based research project posters were presented. After participation in SATELLITES, teachers' content knowledge in geotechnologies and related sciences increased significantly. Teacher's reported an increase in perceptions of their ability to do inquiry science and employ inquiry-based instruction. They also reported a significant increase in student engagement when students collected data and worked through the scientific process while participating in SATELLITES inquiry projects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".