Bayesian Hierarchical Regression to Assess Variation of Stream Temperature with Atmospheric Temperature in a Small Watershed
Bibliographic record
Abstract
This paper described the variability of stream temperature, T s , and compared relationships between T s and air temperature, T a , at 10 sites along a 1.2 km reach in a 2 km 2 basin in New Jersey, USA, using Bayesian Hierarchical Regression. Mean daily mean T s was significantly cooler at two sites and significantly warmer at three sites relative to the mean daily T s for all sites combined. Seasonal daily mean T s showed the greatest variation between sites in the summer within the reach for both daily mean and daily maximum temperatures. Posterior distributions for slope parameters ( β j ) for regressions varied significantly by season and showed the greatest variation in summer. The strongest relationships occurred in autumn with β = 0 . 743 ± 0 . 019 ( β = 0 . 712 ± 0 . 022 ), and the weakest relationships occurred in the summer with β = 0 . 254 ± 0 . 030 ( β = 0 . 193 ± 0 . 039 ). Results support the conclusion that riparian shading impacts the effect of T a on T s , and that T s shows a stronger relationship with measured T a at sites in open areas that are more likely to have meteorologic conditions similar to bulk conditions.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| 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".