Data from: Statistical stream temperature modelling with SSN and INLA: An introduction for conservation practitioners
Bibliographic record
Abstract
Statistical stream temperature models can predict the fine-scale spatial distribution of water temperatures and guide species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence data collected within dendritic networks. We used data from miniature sensors deployed in Canadian Rocky Mountain streams to develop and validate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was based on spatial steam network models (SSNs) specifically developed to account for spatial autocorrelation in dendritic stream networks. The second used integrated nested Laplace approximation (INLA) that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitted SSN and INLA models using leave-one-out cross validation from the data collected along the stream network. Both modelling techniques had similar RMSE and MAE (near 1oC) and r2 (> 0.6) values, and proved flexible with respect to implementation; however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice and open-access data and r-script to help non-experts develop statistical stream temperature models of their own.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.033 |
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".