Eutrophication in Northwestern Ontario? The unique case study of Cloud Lake
Bibliographic record
Abstract
This thesis investigates freshwater lake classification in Northwestern Ontario, Canada. The research is based on a case study of Cloud Lake, 40km south of Thunder Bay Ontario, Canada. Complaints of decreasing water quality brought attention to the need for a detailed assessment of the current conditions on Cloud Lake. The purpose of this thesis was to determine Cloud Lake?s present trophic state based on two currently implemented trophic state indexing (TSI) methods, (Carlson?s TSI, and the Ontario Ministry of the Environment and Climate Changes). \nThis thesis provides biological, chemical, and physical evidence that Cloud Lake?s water quality is a serious concern. Cloud Lake is a mesotrophic lake with confirmed occurrences of toxin producing cyanobacteria. The results underlie a misconception within current monitoring of inland lakes located within Northwestern Ontario that environmental conditions are pristine. Therefore, lakes in the geographic region should all be oligotrophic. Despite the absence of significant anthropogenic inputs (i.e. agricultural or urban development) Cloud Lake demonstrates a number of symptoms associated with eutrophication in larger, more developed lakes that relate to internal loading being a dynamic factor. The thesis provides recommendations for future monitoring and research to better understand the complex causes of eutrophication in Cloud 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".