The Ecological History of Lake Ontario According to Phytoplankton
Bibliographic record
Abstract
Lake Ontario’s ecosystem has been impacted by\nurban sprawl, chemical pollutants, agricultural intensification, land use changes, climate change effects, habitat loss and non‐native species. We present a synthesis of long‐term information to\nreconstruct past stressor impacts, remediation and trajectories of current changes. Paleolimnological and long‐term monitoring studies, particularly those using diatoms (above), reveal long‐term changes, providing a rich understanding of multiple\nstressor effects on primary production including climate‐driven change in lake ecosystems. In this poster we present preliminary analyses from a sedimentary core analysis (above) and monitoring data from recent decades. Approximate temporal zones were derived using cluster analysis of the\nsedimentary assemblages. Primary goals of this investigation are to support management of the lake through a retrospective of stressor impacts.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".