‘Boundary’: mapping and visualizing climatically changed landscapes at Kaskawulsh Glacier and Kluane Lake, Yukon
Bibliographic record
Abstract
This paper describes a collaboration between a visual artist and geoscientists, who together viewed the same rugged, high mountain landscape through different, yet complementary, lenses. We pair scientific mapping and historic comparative photography with a series of site-specific sculptural installations to interpret the dramatic geological changes that occurred at Kaskawulsh Glacier, Yukon, in the spring of 2016. In the summer of that year, artist K.A. Colorado accompanied geoscientists D.H. Shugar, J.J. Clague, and J.L. Best to the terminus of Kaskawulsh Glacier, as well as Kluane Lake downstream of the glacier, to document the landscape changes that occurred earlier in the year. The Boundary images were created as on-site, three-dimensional, artistic interpretations of the markedly changed boundaries that occurred as a result of climate-induced glacier retreat and the sudden subcontinental-scale reorganization of drainage. Both the scientific study conducted by the geomorphologists and the art installations created by the artist were performed simultaneously. The Boundary installation art project, together with satellite imagery and historical photographs, conveys the death of Slims River as a result of climate change.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".