Beyond x,y,z(t); Navigating New Landscapes of Science in the Science of Landscapes
Bibliographic record
Abstract
Abstract At the start of its centennial year, AGU's surface process community revisited G. K. Gilbert's legacy of landscape description and experimental models of surface processes, as well as his embrace of critique and pragmatism in the practice of landscape science. In the 100 years, since Gilbert and especially since the dawn of the 21st century, we have seen an intensified focus on the acquisition of more and more earth observation data and the numerical modeling of landscapes, alongside widespread use of deterministic and predictive practices to find solutions to the social, economic, and environmental challenges of today. What have we gained and lost in this pursuit? Here we lay out some of the challenges for the discipline in an increasingly data‐rich and complex world in which earth science is also being called to reorient itself towards more societally relevant roles. We ask the community to ponder the following: Is the discipline serving our scientific and societal goals, or is there a need for the science of landscapes to adopt new frameworks of thinking and to question the deterministic approaches that have dominated our discipline to date, in order to attend to the needs of living in the Anthropocene?
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".