Data Basin Climate Center: sharing and manipulating spatial information on the web
Bibliographic record
Abstract
Abstract Monitoring datasets is essential to detect changes that are occurring and identify thresholds that cause them, but scientists around the world are now generating large volumes of data that vary in quality, format, supporting documentation, and accessibility. Moreover, diverse models are being run at various spatial and temporal scales to try and understand past climate variability and its impacts, generate future climate and land use scenarios, and project potential future impacts to the planet. Conservation practitioners and land managers are struggling to synthesize this wealth of information, identify relevant and usable datasets, and translate evolving science results into on-the-ground climate-aware strategies.In partnership with ESRI and Mambo media, the Conservation Biology Institute (CBI) is developing a versatile web-based resource that centralizes usable climate change-relevant datasets and provides analytical tools to visualize, analyze, and communicate findings for practical applications. To illustrate its capability to store, manipulate, and derive relevant conclusions to users, we present three examples of projects involving scientists and managers that are part of the Climate Center of Data Basin (http://www.databasin.org): a conservation project in the Puget Sound area of Washington State, a climate change impacts project in California, a North American project looking at climate change impacts on Canada lynx. We conclude by showing the use of this new web tool in workshops that bring together scientists and practitioners, allowing all to access the data and develop more effective management strategies.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".