BENTHIC DIATOM AUTECOLOGY AND INFERENCE MODEL DEVELOPMENT FROM THE CANADIAN HIGH ARCTIC ARCHIPELAGO<sup>1</sup>
Bibliographic record
Abstract
Diatom assemblages were analyzed from 64 lakes and ponds from Alert, Ellesmere Island and Mould Bay, Prince Patrick Island in the Canadian High Arctic Archipelago. Diverse water chemistry conditions and diatom communities were present in these sites. Small benthic taxa typically dominated diatom communities; however, assemblages were markedly different between Alert and Mould Bay sites in response to disparate water chemistry characteristics in the two regions. The most abundant taxa belonged to the genera Navicula, Cymbella, Achnanthes, Nitzschia, and Pinnularia. Canonical correspondence analysis indicated that pH, specific conductivity, dissolved organic carbon, and total phosphorus were the most important limnological variables in determining species composition. Diatom inference models were developed for pH, specific conductivity, and dissolved organic carbon using weighted averaging and weighted averaging partial least squares techniques; these had root mean square error of prediction/r2boot values of 0.40/0.77, 0.28/0.70, and 0.24/0.55, respectively. These models are applicable to sites with large ranges of taxonomic and limnological variation and will allow the reconstruction of past changes of climate‐related limnological parameters from biostratigraphic records in future paleolimnological studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".