Learning to use virtual reality for marine science education
Bibliographic record
Abstract
Virtual reality (VR) can provide experiences that more closely approximate real-life than standard video. It represents a powerful set of communication tools that are seeing increasing use in many areas, including science and education. Currently most undergraduates have limited opportunities to learn how to effectively communicate with VR. This project was funded by a WWU Student Technology Fee grant to provide Western students with tools that would introduce them to using VR for communicating about the marine realm. The collaborators on this project are members of the 2021 cohort of Marine Science Scholars (MSS), which is one of WWU’s distinguished scholars’ programs. Western’s new Marine and Coastal Sciences program, which offers undergraduate degrees in integrative marine science, developed and manages MSS. Starting with a one-week residential stay at the Shannon Point Marine Center last September, these students have been learning about marine science together every quarter of the past academic year. Their studies have focused on the ecology, management, and human history of the Salish Sea. The 360° videos and images they have collected are made available here so that others can experience aspects of local marine habitats that many people never see.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".