DIATOM‐BASED ENVIRONMENTAL INFERENCES AND MODEL COMPARISONS FROM 494 NORTHEASTERN NORTH AMERICAN LAKES<sup>1</sup>
Bibliographic record
Abstract
The relationships between diatom assemblages and important limnological variables were investigated in 494 lakes from northeastern North America (Pennsylvania, USA, to Nova Scotia, Canada). The limnological variable most significantly related to diatom assemblages was lake water pH, although dissolved organic carbon and nutrients were also important. Based on these strong relationships, highly significant diatom‐based inference models were developed to reconstruct key limnological variables based on diatom assemblages using weighted averaging (WA), maximum likelihood (ML), and modern analogs technique (MAT). The performances of the pH‐inference models were high, similar, and significant (WA: r2boot = 0.89, root mean squared error of prediction (RMSEP) = 0.43; ML: r2boot = 0.89, RMSEP = 0.45; MAT: r2boot = 0.89, RMSEP = 0.46). In addition, distribution of sites along a pH gradient did not have the anticipated bias, especially with respect to the WA model, although an evenly distributed study set did result in slightly less noise. While some regionally specific information may be lost by utilizing a large number of lakes from a wide geographic area, the broad limnological gradients allow a more realistic, accurate, and complete description of the ecological characteristics of diatom species. In addition to providing new autecological data on diatoms for northeastern North America, these inference models can now be used to infer accurately and precisely lake water pH and associated variables for limnologists and lake managers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".