Toward the development of an ecosystem model ensemble to support adaptive management in Lake Ontario
Bibliographic record
Abstract
Notwithstanding the continuing advancement of our understanding of the broader ecosystem functioning in Lake Ontario, emerging evidence suggests that there are fundamental knowledge gaps to accurately describe the relationship between exogenous phosphorus (P) loading and in-lake total phosphorus (TP) concentrations. The whole-lake load appears to frequently exceed the Great Lakes Water Quality Agreement (GLWQA) interim target of 7000 metric tonnes·year −1 , although the offshore-water P concentrations are consistently below the GLWQA interim substance concentration objective of 10 µg TP·L –1 . Contrasting ecological conditions prevail in different locations of Lake Ontario, owing to the significant urban footprint along the shoreline but also the capacity of dreissenid mussels to sequester P in the littoral zone. Specifically, low ambient P levels threaten fisheries productivity in offshore waters, while nuisance benthic algae (predominantly Cladophora) and toxin-producing cyanobacteria blooms affect the water quality in the nearshore zone. The present study offers a technical analysis of the recent and ongoing modelling work that has been conducted in Lake Ontario and can be potentially used to address the multitude of ecosystem management challenges. Our aim is to provide an overview of all the major models developed by identifying their fundamental assumptions, structural attributes, and general consistency against empirical knowledge derived from the system. The existing modelling work has opted for parsimonious representations of the lower food web coupled with granular grid configurations to effectively link hydrodynamic processes and mass transport between nearshore and offshore waters. The establishment of comprehensive ecophysiological modules that will recreate the mechanisms underlying the interplay among bioavailable phosphorus, planktonic dynamics, dreissenid mussels, and Cladophora is a critical undertaking to reproduce the water quality conditions in the nearshore zone of Lake Ontario. Striving to integrate the lower food web with fisheries and ecosystem-service modelling, we also offer a technical analysis of knowledge gaps and monitoring-assessment objectives that should be addressed to ensure that ecosystem processes of management interest are adequately measured and the local modelling enterprise is focused on suitable performance indicators.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".