The Distribution of Freshwater Chironomidae (Insecta: Diptera) across Treeline near the Lower Lena River, Northeast Siberia, Russia
Bibliographic record
Abstract
Surficial sediment from 31 lakes along a transect spanning treeline in northeast Siberia was analyzed for midge remains in order to assess the modern distribution of midges relative to treeline. Taxa distinct to tundra, forest-tundra, and forest areas were identified. Abiskomyia, Parakiefferiella nigra, and Hydrobaenus/Oliveridia were found predominantly in tundra lakes, whereas Zalutschia zalutschicola and Microtendipes were restricted to forest-tundra or forest lakes. A sharp delineation exists at the tundra/forest-tundra transition zone with respect to the genus Corynocera. Corynocera oliveri was found chiefly in tundra lakes whereas C. ambigua was found solely in forested areas.Thirty-two environmental variables describing the physical, chemical, and limnological characteristics of the lakes in the transect were measured. Redundancy analysis (RDA) revealed that statistically significant relationships exist between chironomid distributions and six of the measured environmental variables (particulate organic carbon, particulate organic nitrogen, iron, zinc, lake depth, and Secchi depth), but not surface lake-water temperature. Canonical variate analysis (CVA) demonstrated that chlorophyll a, lake depth, pH, and strontium maximized separation of tundra, forest-tundra, and forest lakes from one another. These results illustrate the importance of treeline as an ecological boundary for the distribution of chironomids. The abrupt changes in distribution that occur at treeline for specific chironomid taxa suggest that subfossil chironomid analysis may be used to infer past changes in the position of treeline.
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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 | 0.001 | 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) | 0.001 | 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".