Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages
Bibliographic record
Abstract
<i>Abstract.</i>—Lakes are common features on the landscape in many regions of the world and have important impacts on the ecological function and habitat conditions of connected rivers and streams. Yet lakes are rarely incorporated in studies of river and stream network structure. Using stream and lake spatial data of catchments in Ontario, metrics can be developed that quantify the spatial structure of complex stream–lake networks. This case study represents the initial step in a larger project to develop a complete, parsimonious set of metrics that would make the link between stream–lake network structure and aquatic ecosystem function at the landscape scale. We present three new metrics that capture essential information about the spatial distribution and size of lakes in aquatic networks. These metrics can be used as descriptive measures to understand differences among catchments and as explanatory variables in predictive modeling. Developing stream–lake network measures is an important first step towards integrating lakes and streams for aquatic assessments, improving our ability to effectively manage and conserve aquatic ecosystems in many parts of the world.
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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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".