Examining the pluvial to nival river regime spectrum using nonlinear methods: Minimum delay embedding dimension
Bibliographic record
Abstract
The nonlinear dynamics of streamflow time series from 667 reference hydrometric stations in North America spanning the pluvial-nival hydrological continuum are explored using minimum embedding dimensions as determined by False Nearest Neighbor (FNN) methods. Simulations using synthetic time series demonstrate that snowmelt dominated time series have lower embedding dimensions than those that are rainfall dominated, and that mixtures of the two processes result in a nearly linear change in the embedding dimension. The majority of the reference hydrometric stations drop below a 1% threshold at a dimension less than 30, showing a high degree of natural complexity in the signals ranging from annual snowmelt to weather-driven pseudo-stochastic systems. A less restrictive threshold, 5% is suggested to be more appropriate for streamflow time series. Time series smoothing impacts the embedding dimension and over-smoothing results in incorrect reductions of embedding dimensions. The relationship of the embedding dimensions to watershed and statistical properties of streamflow record showed the lowest embedding dimensions are restricted to large drainage areas, high elevations, and large mean annual flows and variance, high autocorrelations, and large fractions of the records with only small changes in magnitude. Times series that have a large proportion of consecutive days of equal streamflow typically result in higher embedding dimensions. Mapping of the embedding dimension shows spatial patterns related to the streamflow generating processes and geographical features. The use of embedding dimension resolved different dynamics across the hydrological continuum for rainfall to snowmelt over many climate zones. Changes in the embedding dimension might indicate process changes related to climate variability and change.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".