Analyzing streamflow variation in the data-sparse mountainous regions: An integrated CCA-RF-FA framework
Bibliographic record
Abstract
In this study, an integrated CCA-RF-FA framework (abbreviated as CRFF) is developed for analyzing the streamflow variation in the mountainous watershed. CRFF incorporates cross-correlation analysis (CCA), random forest (RF), and factorial analysis (FA) within a general framework. CRFF can identify the time lag effect in the runoff mechanism (both rainfall and glacier/snow meltwater), tackle the problem of the simulation performance degradation caused by the time lag effect, as well as investigate the individual and interactive effects of meteorological and physical factors on runoff simulation. CRFF is applied to the Amu Darya River Basin (ADRB), a typical mountainous watershed in Central Asia. Bayesian neural network (BNN) and stepwise cluster analysis (SCA) are used for illustrating the advantage of RF in streamflow simulation. The main findings reveal that (i) compared with BNN and SCA, RF has the better simulation capacity; (ii) the time lag effect of heat conditions such as temperature (T) and shortwave radiation (SWR) is weak, with the lag time being less than 30 days; the time lag effect of the precipitation conditions such as snow precipitation rate (SPR) is strong, with a lag time ranging from 30 days to 90 days; (iii) in snow-melting, non-melting and entire periods, T has the dominant impact on the variation of the runoff in ADRB; (iv) the interaction of T and SWR has important effect on the streamflow; SPR can still considerably affect the runoff generation in non-melting period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".