Lake Superior cyanobacterial bloom reports, 2012 - present
Bibliographic record
Abstract
Lake Superior is a cold, oligotrophic lake. It is the largest freshwater lake in the world by surface area and the largest of the Laurentian Great Lakes with binational (United States and Canada) and multi-state (Michigan, Wisconsin, Minnesota) borders. Lake Superior is a globally important freshwater resource. Beginning in 2012, researchers and resource managers began receiving reports of cyanobacterial blooms in the western arm of the Lake. Cyanobacterial blooms have been reported nearly every year since. The purpose of this dataset is to collect observations of cyanobacterial blooms for Lake Superior and connecting waters, document patterns in their occurrence, and provide insights into their causes. For this dataset, a cyanobacterial bloom is defined as an aggregation of cyanobacterial biomass in some or all of the water column, which may lead to the occurrence of surface scums or subsurface maximums. This dataset relies on observations, data, photographs, and insights from a wide range of agency staff and individual members of the public with key input from participants in the Lake Superior Algal Bloom and Nutrient Subgroup collaboration. This dataset only includes reported blooms, and likely doesn’t include all actual bloom events. Additionally, there may be more than one report for a single bloom event, depending on its size. For example, in 2018 there was a widespread bloom event which resulted in numerous reports. Interpretation of this dataset should incorporate these nuances. Observations in the dataset were assigned one of four verification statuses to communicate the level of certainty in the cyanobacterial bloom event. Additional information on taxonomy and/or toxin analyses can be made available upon request.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".