Bibliographic record
Abstract
The Great Lakes Surveillance Program has been monitoring water quality for almost 40 years in Lake Ontario. The program provides some of the most comprehensive, systematic and detailed information that is available in the world for such a large lake. The water quality in Lake Ontario has shown dramatic changes over the last 40 years, with the early measurements indicating high phosphorus concentrations that were subsequently reduced by management responses to the Great Lakes Water Quality Agreement. Other water quality parameters, such as some of the major ions, showed reductions during the 1970s and 1980s as well. Nitrate plus nitrite nitrogen has increased in the lake throughout the period of record, likely driven by increasing watershed and atmospheric sources. A major driver of more recent trends in water quality appears to be the invasion and subsequent expansion of invasive Dreissena mussel populations that first appeared in Lake Ontario in 1989. Total phosphorus concentrations have further declined, and the proportion of total phosphorus that is soluble is increasing, possibly due to the filtering action of these mussels. Concentrations of major ions that are incorporated in mussel shells such as calcium have declined, while those that do not, such as magnesium, have increased. Spring silica concentrations are increasing; an ominous signal of declining diatom populations, which may also be a symptom of the proliferation of invasive mussels in the 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".