Geostatistical regional trend detection in river flow data
Bibliographic record
Abstract
Abstract Many studies have identified global warming and climate change as some of the biggest challenges facing Canada. In this paper, the regional temporal trend in river flows is investigated using a space–time model. Though the primary focus is the time component, the spatial relationship among monitoring stations in a region is used to develop a space–time model that is composed of a random time trend as a function of space, and a random error term as a function of both time and space. The estimate of regional time trend is a linear combination of the differenced observations that minimizes the variance of estimated errors. Data from 248 river stations in the Reference Hydrometric Basin Network (RHBN) established by Environment Canada is analysed. These hydrological monitoring stations are grouped into ten non‐overlapping homogeneous regions covering all of Canada. An estimate of trend, along with its variance, is calculated for each region. Some significant trends are found for the annual mean, maximum and minimum flows, as well as for the mean monthly flows for July and December, and are consistent with those detected in other Canadian studies. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".