Managing a Lake That Flows Both Ways
Bibliographic record
Abstract
Vancouver Lake, an urban eutrophic lake in Vancouver, WA, is considered a regional “gem” but, despite decades of research and restoration efforts, the lake and its users continue to suffer from a variety of issues including annual toxic HABs and noxious aquatic weeds. Championed by the local Friends of Vancouver Lake group, the state legislature has awarded Clark County funds to restore Vancouver Lake through the development and implementation of a management plan. Development of the 2023 Vancouver Lake Management Plan (VLMP) included engaging technical experts and local stakeholders, and evaluating feasible management alternatives using lake modeling and cost-effectiveness analyses. One of the key recommendations of the VLMP, based on the results of these analyses, called for enhanced lake flushing to reduce toxic HABs. Today, the project team, technical experts, and local stakeholders are working to 1) develop a funding and governance structure to ensure sustainable, long-term lake management; 2) evaluate the efficacy of piloted near-term beach management solutions to minimize risks to public health; 3) design flushing enhancement options; and 4) update and refine the linked hydrodynamic (HEC-RAS 2D) and water quality (WASP) model to further inform the feasibility of flushing options and advance implementation of long-term solutions. We encountered a number of social and scientific challenges throughout this project. In this presentation, we will summarize project goals and progress to date, and describe the successes and lessons we’ve (l)earned along the way. Through innovative problem-solving and collaboration, we’re excited to move forward together to restore Vancouver Lake.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".