0 1998, by the Amencan S~iefy of Limnology and Oceanography, Inc Biologically induced calcite and its isotopic composition in Lake Ontario
Bibliographic record
Abstract
We studied water, sediment trap, and core samples from eastern Lake Ontario to reconstruct the factors controlling the biologically induced production and sedimentation of calcite during so-called whiting events. Calcite accumulation and its isotopic composition are controlled by a complex set of interrelated factors, including temperature, primary productivity, and the abundance of pica-cyanobacteria during the stratified period. Calcite precipitation is highly correlated to lake temperature, because physical and biological factors interact to produce conditions favorable for whitings during warm years when the lake stratifies early in the seasonal cycle. Carbonate stratigraphies in multiple cores from eastern Lake Ontario revealed similar patterns of historical variation in percent carbonate. An exponential rise in carbonate accumulation occurred in nine cores after 1930, culminating in peak values in the early 1980s. This rise was related to historic increases in primary productivity resulting from increased phosphorus loading to Lake Ontario. Superimposed upon this rise were four peaks (centered on 1940-1942, 1957-1961, 1971-1977, and 1983) that correlate with maxima in summer air-temperature anomalies from the Great Lakes region and with strong El Niiio events. These peaks are also associated with maxima in S’C values and minima in S180 values of carbonate, lending support to our model that more calcite is precipitated with higher 6’C values during warm years when thermal stratiti-
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".