Bibliographic record
Abstract
A 2009 study revealed serious flaws in a standard technique used by hydrological researchers to understand how changes in watershed land use affect stream flow behaviors, such as peak flows. The study caused academics and government agencies alike to rethink decades of watershed research and prompted Kuraś et al. to reinvestigate a number of long‐standing assumptions in watershed research using a complex and well‐validated computer model that accounts for a range of internal watershed dynamics and hydrologic processes. For the test site at 241 Creek in British Columbia, Canada, the authors found not only that deforestation increased the severity of floods but also that it had a scaling influence on both the magnitudes and frequencies of the floods. The model showed that the larger the flood, the more its magnitude was amplified by deforestation, with 10‐to 100‐year‐return‐period floods increasing in size by 9%–25%. Following a simulated removal of half of the watershed's trees, the authors found that 10‐year‐return‐period floods occurred twice as often, while 100‐year‐return‐period events became 5–6.7 times more frequent. This proportional relationship between the increase in flood magnitudes and frequencies following deforestation and the size of the flood runs counter to the prevailing wisdom in hydrological science.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".