FACTORS INFLUENCING DIATOM DISTRIBUTIONS IN CIRCUMPOLAR TREELINE LAKES OF NORTHERN RUSSIA
Bibliographic record
Abstract
Diatom assemblages and limnological data were analyzed from 74 lakes spanning arctic treeline in three geographical regions of northern Russia: near the mouth of the Pechora River, on the Taimyr Peninsula, and near the mouth of the Lena River. Analysis of similarities indicated that diatom assemblages in tundra and forest lakes were significantly different from each other in all regions, with tundra lakes generally associated with higher abundances of small benthic Fragilaria Lyngbye taxa. Canonical correspondence analysis identified variables related to ion concentrations (e.g. Na+, dissolved inorganic carbon), lake depth, silica concentrations, and surface water temperatures as factors that explained significant amounts of variation in the diatom assemblages. Across treeline, the generally higher surface water temperatures of the forested lakes consistently accounted for a significant proportion of the diatom distribution patterns. Major ion concentrations also explained significant amounts of variation in the diatom assemblages across treeline for all three regions; however, regional trends were most likely influenced by local factors (i.e. ocean proximity or anthropogenic activities). The importance of climatic gradients across treeline (e.g. temperature) diatom distributions provides additional evidence that diatoms may be useful as paleoclimatic indicators. However, combination of the three calibration sets revealed that local water chemistry determinants (e.g. lithology, marine influence) overrode the influence of climatic gradients in explaining diatom distributions, suggesting that regional differences must be minimized for successful combination of geographically separate calibration sets.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".