Cloud-based geospatial services for building capacity and safeguarding heritage in climatically marginal landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our world is changing rapidly, and nowhere is this transition more pronounced than in the climatic extremes of our planet. For the people who call these places home, the myriad threats facing their rich cultural landscapes in the context of the current climate change crisis—rising sea levels, fluvial erosion, drought, sand dune encroachment—are becoming a source of great social anxiety. Furthermore, these environmental pressures are compounded by population growth and urban development. Using two contrasting study regions, the Yukon-Kuskokwim Delta in Alaska, USA and Mauritania, we explore how free cloud-based geospatial services such as Google Earth Engine (GEE) might be used to build capacity for communities in the Arctic and the Sahel. We present five analytical remote sensing tools built in GEE, each one designed to address specific and urgent environmental concerns in the regions in question.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it