An Insight from Organizing CLImbing for CLImate GEOspatial School (CLIGEOS-2024) in Mountainous Regions
Bibliographic record
Abstract
Abstract. This paper delves into the multifaceted experiences and insights garnered from organizing a short-term training programme aimed at professionals, students, academics, and climate enthusiasts. The programme, named CLImbing for CLImate GEOspatial School (CLIGEOS-2024), was a collaborative effort by the International Society of Photogrammetry and Remote Sensing (ISPRS) and ISPRS Student Consortium (ISPRS-SC) alongside Center for space science and geomatics studies (CSSGS) Pashchimanchal campus, Institute of Engineering, Tribhuvan University, Nepal, Universita’ Degli Studi Di Udine, Italy and LJ University, India aimed at addressing environmental challenges, particularly in hill and mountain regions. Despite facing logistical and communication hurdles due to the geographical dispersion of organizing institutions, the event successfully brought together participants with backgrounds in GIS, remote sensing, agriculture, and forestry. The programme was structured to promote participatory learning and project-based learning methodologies, incorporating classroom teaching, hands-on activities, fieldwork, data collection, short projects, quizzes, and evaluation mechanisms. Through a meticulously planned sequence, participants engaged in theoretical lectures, practical sessions on drone technology, industry expert presentations, and a mountain trek to observe climate change phenomena first-hand. Despite challenges related to infrastructure, including limited internet connectivity and access to data, the programme served as a catalyst for raising awareness about climate change and promoting sustainable practices. Key takeaways highlighted the importance of spatial data analysis, interdisciplinary collaboration, and hands-on learning in advancing solutions for sustainable development. The paper concludes with lessons learned from the programme, providing insights for future initiatives aimed at capacity building and knowledge dissemination.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".