The use of advanced and emerging technologies for adaptive ecosystem-based management of the Great Lakes
Bibliographic record
Abstract
Abstract The Great Lakes and connecting waters encompass a vast and diverse ecosystem that presents scale challenges for management similar to those of the coastal ocean. Technological approaches to overcome the scale challenges have primarily been adapted from oceanographic applications and technologies, and from upscaling inland lake methods designed for shallower and calmer water bodies. Many standard methods for studying Great Lakes habitat and biota have long lag times between field collection and data availability. Many also miss much of the dynamics, three-dimensional complexity, and spatial variability needed to manage the system effectively. Even baseline conditions are not well characterized for many parts of the Great Lakes ecosystem (e.g. bathymetry and critical habitat, life cycles and food webs, night and winter movement and activity of organisms). Emerging technologies are beginning to address these needs but require coordination, consistent investment, training, and governance linkages. Here we survey recent technological advances and show how they are contributing to improved adaptive management of the Great Lakes ecosystem by reducing uncertainty and increasing understanding of physical, biological, and chemical processes, and the human dimensions of resource management and restoration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".