Ecological patterns of diatom assemblages from Mackenzie Delta lakes, Northwest Territories, Canada
Bibliographic record
Abstract
Sediment samples were collected from 77 lakes in the Mackenzie Delta representing a gradient of lakes from those having continual connection with the sediment-laden Mackenzie River to lakes having connection for only a couple of days every few years. Diatom assemblages in all lakes were dominated by a diverse benthic microflora, primarily from the genera Navicula and Nitzschia. Maximum relative abundance of the dominant taxon at all sites was less than 30%, and most taxa did not dominate in more than one or two lakes. Delta lake assemblages were distinct from diatom assemblages associated with other regional transects of upland tundra and forest lakes. Detrended correspondence analysis showed that nonmotile epiphytic genera, such as Cocconeis and Gomphonema, were more common in lakes having a lower influence from the Mackenzie River, reflecting the extensive macrophyte growth within these lakes. Species diversity decreased as macrophyte production increased. Taxa responses along this macrophyte production gradient were modeled using partial least squares regression. Diatoms were sensitive to the degree of river influence, and the related biological and limnological changes, suggesting assemblages can provide an indication of hydrological variability within Mackenzie Delta lakes.Key words: diatoms, detrended correspondence analysis, Mackenzie Delta, floodplain, lakes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".