No monsters, no miracles: in nonlinear sciences hydrology is not an outlier!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The end users of hydrological models may be justified for being tired of the excessive uncertainty of these models, not to mention their simplistic approximations and crude modelling. The ever-increasing sophistication of model parameter fitting is simply a smoke-screen that hides the models' lack of physical basis, their scale dependence, and their inability to fit widely diverse behaviours. More generally, we have to admit a lack of qualitative improvement in hydrological modelling in recent times. In fact, operational hydrology may have suffered for some time from ignoring the advances in theoretical hydrology, which have, in contrast, greatly stimulated the nonlinear sciences. For instance, more than a century ago fractals were considered as geometrical monsters, whereas decades ago river networks became classical fractal objects, and rainfall and discharges are now classical examples of multifractal fields. These hydrological characteristics are still often ignored by operational hydrology, whereas they explain not only its current limitations, but also how to overcome them. To illustrate these problems, this paper focuses on the fact that hydrological fields are most likely singular with respect to measures of time and volume. This would not only explain the ubiquitous scale dependence of hydrological observations, but would also give the possibility to transform them into scale-independent quantities. The upscaling of a rainfall time series from an hour to a year is therefore discussed in detail, and enables us to quickly introduce other examples. Citation Schertzer, D., Tchiguirinskaia, I., Lovejoy, S. & Hubert, P. (2010) No monsters, no miracles: in nonlinear sciences hydrology is not an outlier! Hydrol. Sci. J. 55(6), 965–979.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 it