Development of a Midge-Based Summer Surface Water Temperature Inference Model for the Great Basin of the Western United States
Bibliographic record
Abstract
Surface sediment recovered from 51 lakes in the Uinta Mountains of northeast Utah was analyzed for subfossil chironomid remains, and incorporated in a midge-based inference model for summer surface water temperature (SSWT). The lakes in the calibration set spanned elevation, depth, and summer surface water temperature ranges of 900 m, 12.7 m, and 11.3°C, respectively. Redundancy analysis (RDA) identified four variables, SSWT, depth, specific conductivity, and Al concentration, that could account for a statistically significant amount of variance in the chironomid distribution, with SSWT accounting for the largest amount of variance. The Uinta Mountain calibration set was merged with a previously developed calibration set from the Sierra Nevada, California, in order to develop a midge-based inference model for SSWT applicable to subfossil chironomid stratigraphies from the Great Basin. A variety of statistical approaches, such as weighted averaging (WA), weighted averaging-partial least squares (WA-PLS), and partial least squares (PLS) were used to assess model performance. The best inference model for SSWT, based on a 3-component WA-PLS approach, had robust performance statistics (r2 jack = 0.66, RMSEP = 1.4°C). The newly expanded inference model will enable more accurate estimates of late Pleistocene and Holocene thermal regimes and help address many outstanding questions relating to long-term and recent climate change in this region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".