Tomatosphere - Mission to Mars An Evaluation of A Space Science Outreach Program
Bibliographic record
Abstract
Tomatosphere is an educational outreach project designed to involve students in scientific research related to long-term space travel. The ultimate goal is the growing of crops on the Moon and Mars. Tomatosphere focuses on the germination of seeds that have either been at the International Space Station or subjected to a simulation of the environment of Space or Mars. The project started in 2001 with 2700 classrooms and has grown to more than 11 000 classes in 2009 (and 13 000 projected for 2010). In the eight year period, the project has touched more than 1 530 000 students, mostly in Canada, but also from the United States and some schools in other countries. Tomatosphere engaged in an arms-length evaluation by teacher participants. They lauded the program and indicated that it increased students’ interest in science (98%), reinforced the scientific method (97%), met their classroom needs (92%), and matched their curriculum needs (96%). Teachers evaluate the project every year when they submit their results. The registration process, teacher’s guide and web site are all rated as excellent. Tomatosphere will continue in its present format in 2010 and then will re-evaluate its direction for the future.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".