Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions?
Bibliographic record
Abstract
Despite the overwhelming tendency in paleolimnology to use both planktonic and benthic diatoms when inferring open-water chemical conditions, it remains questionable whether all taxa are appropriate and necessary to construct useful inference models. We examined this question using a 75-lake training set from Quebec (Canada) to assess whether model performance is affected by the deletion of benthic species. Because benthic species are known to experience very different chemical conditions than their planktonic counterparts, we hypothesized that they would introduce undesirable noise in the calibration. Surprisingly, such important variables as pH, total phosphorus (TP), total nitrogen (TN), and dissolved organic carbon (DOC) were well predicted from weighted-averaging partial least square (WA-PLS) models based solely on benthic species. Similar results were obtained regardless of the depth of the lakes. Although the effective number of occurrence (N2) and the tolerance of species influenced the stability of the model residual error (jackknife), the number of species was the major factor responsible for the weaker inference models when based on planktonic diatoms alone. Indeed, when controlled for the number of species in WA-PLS models, individual planktonic diatom species showed superior predictive power over individual benthic species in inferring open-water chemical conditions.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".