Bibliographic record
Abstract
Ecosystems have been profoundly shaped by unusually rapid climate change effects largely driven by human activities that release heat-trapping greenhouse gases into the atmosphere. The goal of this research is to develop a strategy to measure the direct effects of climate change on the value of natural resources, particularly Great Lakes water resources; and how humans control these resources through management decisions. This base will assist in developing and supplying the tools and information necessary for decision-making to facilitate enhancements and thus policy revision. The Canada-US Great Lakes Water Quality Agreement (GLWQA) had substantial influence on the cleanup and restoration of the region, however, threats to the Great Lakes in the face of climate change demand a renewal of program and policy approaches to the restoration of beneficial uses as identified in Annex 2. To remedy this, climate models including Statistical Downscaling (SDSM) and Artificial Neural Network (ANN) are developed to produce daily predictions of future climate variables at the regional scale. In this study, separate downscaled precipitation and temperature scenarios are generated using the SDSM and ANN with the calibrations and validations derived from CGCM and Hadley models for Canadian Areas of Concern. Then the Delphi Survey Method was designed and administered participants to verify on significant pressures associated with climate change on related beneficial uses of the Great Lakes. Collaborating both data sets allows for a thorough picture of the effects of climate change and possible adaptation strategies in the Great Lakes required to develop management and sustainable public policies
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".