Formational processes of recent, arsenic rich, ferromanganese lacustrine precipitates in Nova Scotia and Northern Ontario
Bibliographic record
Abstract
Banded iron - and manganese - rich precipitates were collected from the lake bottoms of Lake Charlotte (Nova Scotia), Lake Shebandowan (Ontario) and Sowden Lake (Ontario). Investigations of study areas at the macro, meso and micro scale were conducted to understand the iron-manganese rich- nodules in their natural environment. The nodules appear as circular precipitate bands which alternate between high concentrations of iron and manganese. Analysis of precipitates revealed that those from Lake Charlotte are highly concentrated in arsenic. Lake Shebadowan and Sowden Lake samples are highly concentrated in phosphorous. \nCorrelation between iron, arsenic and phosphorous suggests oxidation and precipitation of these elements in the same bands of the nodule. Iron relies on the Eh and pH of an environment to precipitate from solution. At a redox boundary in a near neutral environment, iron is able to oxidize as a sediment coating and co-precipitate arsenic and phosphorous from the water. An affiliation between manganese, barium and cobalt suggests precipitation of these elements in the alternate bands present in the nodule. Barium and cobalt are able to co-precipitate with manganese by either penetrating a manganese oxide by means of protonation, or oxidize and become interchangeable with Mn4+. \nThe growth of the nodules at Shebandowan and the majority of Lake Charlotte sites were probably affected by a redox boundary created by the diffuse upward flow of groundwater with lower Eh than the oxidized lake water. It is likely that photosynthetic and iron and manganese oxidizing microorganisms are present in a bacterial mat covering the nodules and probably played a role in their precipitation. Analysis of the growth mechanisms of precipitates revealed in Sowden Lake and the Granite Islands site of Lake Charlotte were inconclusive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".