Bibliographic record
Abstract
The Canada-Ontario Agreement (COA) on Great Lakes Water Quality and Ecosystem Health (2014) is the 5 year federal-provincial agreement to help meet Canada's obligations under the Canada-US Great Lakes Water Quality Agreement (GLWQA). The first goal of COA Annex 8 Groundwater Quality is to gain a better understanding of how groundwater influences Great Lakes water quality and ecosystem health, and to identify priority areas for research and investigation. The COA commitment to meet this goal included Ontario and Canada working with the United States to develop the state of groundwater science report. Groundwater science relevant to the Great Lakes Water Quality Agreement: A status report was released in May 2016 and is available on www.binational.net. The report is a product of collaboration among groundwater experts from both countries and summarizes current knowledge on groundwater and identifies science needs to better understand the role of groundwater in the Great Lakes Basin. The short term (2017-2019) science needs that were identified by the Annex 8 team include: 1: Develop better tools to assess groundwater - surface water interaction and use them to advance assessment of regional-scale groundwater discharge (quantity) to surface water in the Great Lakes Basin; 2: Establish science-based priorities to advance the assessment of the geographic distribution of known and potential sources of groundwater contaminants relevant to Great Lakes water quality, and the efficacy of mitigation efforts; 3: Advance monitoring, surveillance, and assessment of groundwater quality in the Great Lakes Basin. Ontario funded COA projects that address the short term science needs for groundwater are described. The University of Guelph is developing an integrated groundwater-surface water model that is based on the extensive water and climate data from a COA funded integrated water and climate monitoring station. The current water cycle and future water cycles under various climate scenarios will be investigated. The Provincial Geomatics Services Centre has identified over 150 databases and inventories of potential point sources of groundwater contamination in southern Ontario. The development of a methodology to assess the results will be developed by COA Annex 8 team. The GLWQA Annex 8 team was involved with developing the Groundwater Quality Subindicator under GLWQA Science Annex 10. Using concentrations of common groundwater contaminants chloride and nitrate an assessment of groundwater quality in the Great Lakes Basin was conducted for the first time.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".