Grand challenges for research in the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract The Laurentian Great Lakes (LGL) constitute one of the largest freshwater systems in the world while providing social and economic value to two powerful nations. The spatial scale of these inland seas falls between two endpoints: small lakes and oceans. Lacustrine in many characteristics, the LGL often require a scientific approach with attributes similar to those of oceanography. There is a strong sense that within the LGL support for scientific research has not kept pace with the need for process‐oriented research and that we lack basic information needed to forecast change, mitigate impacts and restore and preserve the LGL. Consequently, 58 researchers met in September 2014 and identified five “Grand Challenges for Research in the LGL”: (1) How has this vast inland freshwater system responded to shifting climate in the past, and how will it respond in the future? (2) What is the current status of the most important ecosystem processes, including their variability in space and time? (3) What processes are characteristic only of large lakes, and how do the distinct habitats integrate into a whole? (4) What are the ecosystem responses to major anthropogenic forces such as nutrients and invasive species, and are these reversible? and (5) What are the small to large‐scale linkages and feedbacks among societal decisions, biological systems, and physicochemical dynamics? An urgent need exists for a unified scientific voice that articulates the Grand Challenges for research in the LGL and the need for associated funding. This treatise describing the Grand Challenges develops that voice.
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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 | 0.045 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".