Applying the reference condition approach to Lake of the Woods: Sediment and benthic invertebrate community assessment for lake-wide management
Bibliographic record
Abstract
McDaniel T, Pascoe T. 2017. Applying the reference condition approach to Lake of the Woods: Sediment and benthic invertebrate community assessment for lake-wide management. Lake Reserv Manage. 33:000–000. Lake of the Woods (LOW) is a large, international lake recently designated as impaired by the State of Minnesota due to excess nutrients and nuisance algal blooms. Concerns regarding the impacts of eutrophication have prompted the need for management tools to help to defines areas of ecosystem impairment and to monitor changes in trophic status. The goal of this study was to assess areas of potential anthropogenic impacts in LOW using a benthic macro-invertebrate reference condition approach model and identify factors correlating with these impacts. We also sought to provide baseline information on sediment chemistry prior to the initiation of increased mining activity in the basin. A Canadian Aquatic Biomonitoring Network (CABIN) reference model was developed for LOW to compare the benthic community structure at a number of potentially stressed or impaired sites. Concentrations of both nutrients and metals in sediments at many sites in LOW exceeded Ontario provincial and Canadian federal effect levels for aquatic life. The benthic community at some locations was found to be divergent from reference sites, with substantial reductions in diversity and abundance associated with stress to the benthic community. As expected, benthic invertebrate diversity appeared to be most affected at sites that were deep, thermally stratified and high in nutrients thus making them prone to hypoxia. Benthic diversity was also negatively associated with higher concentrations of metals such as lead and arsenic. The CABIN approach can provide a useful tool in lake management for the identification of stressed sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".