Field trip to at Makgadikgadi Salt Pans- a multimedia outreach project to capture the day-to-day human story of planetary science, to raise awareness of Europlanet and of the diverse research projects taking place at a planetary field analogue.
Bibliographic record
Abstract
There is a societal obligation to communicate planetary science activities to the general public. Many science and technology breakthroughs go unnoticed too frequently due to a lack of interest or awareness from the general public and an inability of scientists to engage broad audiences effectively. In July 2023, science communicator Niamh Shaw accompanied field geophysicist Mebatseyon Shawel on a six-day TA2023-funded field trip entitled 'Investigation of geomorphic features in Ntwetwe pans, Makgadikgadi Basin, Botswana, using Ground Penetrating Radar: implications for Martial surface landforms'. The expedition to the Makgadikgadi salt pan was led by Fulvio Franchii and team at BUIST, Botswana. The purpose of a science communicator as documentarian was to capture the story of one science team on a field trip to the Makgadikgadi Salt pans. The overall objectives of the science communication study were to capture public interest in planetary science taking place in Botswana, the Makgadikgadi Salt pans and space, through focussing on telling the real-time story of a science analogue pre-, during- and post-mission, highlighting the importance of Analogue sites, specifically the Makgadikgadi Salt Pans, and the opportunity for non-Space audiences individuals to better appreciate planetary science. Using a number of media forms the story of this six day field trip was captured through video, photography, audio and personal testimony. The story of the field trip was presented to schools and family audiences in Brazil, Ireland, Canada and the USA. The presentation capably captured the public’s interest by telling the human story of a science mission conducting a field trip, as evidenced by many follow-up questions. There was little or no knowledge of planetary science or analogues in the audience prior to the presentation. Photographs of the mission were included in science publications post-field trip. The use of personal testimony as a method for informal science learning is an effective and original approach to highlight the importance of planetary science analogues to the general public. In simple terms, it was found that seeing a daily account of what scientists do, is immediate and accessible to viewers and readers.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.260 | 0.050 |
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".