Bibliographic record
Abstract
A traditional ecological knowledge summit The Global Center for Climate Change and Transboundary Waters (GCTW) cohosts a Traditional Ecological Knowledge Summit (1), as Gail Krantzberg (2), Peter Czajkowski, Dawn Martin-Hill, Rohini Patel, Hiliary Monteith, and Drew Gronewold explain. The Global Center for Climate Change and Transboundary Waters (GCTW) integrates hydroclimate modeling, water quality forecasting, and community-engaged mixed methods that harmonize and propagate Traditional Ecological Knowledge (TEK), Indigenous Knowledge (IK), and Western Science (WS) into robust 21st-century transboundary water resources governance protocols. The U.S. National Science Foundation and the Canadian Social Science and Humanities Research Council fund the Center. It supports a multinational network of researchers that is designed to promote information sharing across borders.
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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".