Bibliographic record
Abstract
On examining the scientific literature of recent years, one notices an increase in the number of studies of global warming and its impact on the Earth’s various environments. Research has been undertaken in various fields such as geomorphology, hydrology and many others. In the context of climatic change, there is growing interest in the study of past floods, or palaeofloods. Researchers are attempting to reconstitute the chronology of past floods, especially with respect to past or subrecent climatic changes. The work involves using different methodological approaches borrowed from various disciplines including geology, geomorphology and ecology. The reconstruction of ancient hydrological events such as palaeofloods in fact requires that different methods and techniques be combined in order to trace the chronology of events as precisely as possible using different biophysical parameters. A wide variety of indicators are used in the chronological reconstruction of ancient fluvial environments, whether in humid, subhumid or desert regions. These indicators involve analysing stratigraphic sequences and sedimentary deposits, organic matter and macrorest deposits, as well as using radiocarbon dating(14C), thermoluminescence (TL), and dendrochronology and lichenometry. In fact, most work on the reconstitution of the frequency and magnitude of ancient floods uses several methods and techniques to obtain the long-term chronology of flood events in relation to the specific conditions (e.g., climate, geomorphology) of a region or study area. With the publication of several studies in palaeohydrology, it was interesting to examine, through a literature review, the various approaches used in the study of palaeofloods. This kind of study has seen major advances, which can be explained partly by the interest generated by global climate change and its effect on river-system dynamics.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".