Basinga: A cell‐by‐cell GIS toolbox for computing basin average scaling factors, cosmogenic production rates and denudation rates
Bibliographic record
Abstract
Abstract The calculation of denudation rates from the measured cosmogenic nuclide concentrations in river sediments requires assumptions and approximations. Several different approaches and numerical tools are available in the literature. A widely used analytical approach represents the muogenic production with one or two exponentials, assumes the attenuation length of muons to be constant and also neglects temporal variations in the Earth's magnetic field. The denudation rates are then calculated directly and analytically from the measured concentrations. A second numerical and iterative approach was more recently proposed and considers a more rigorous muogenic production law based on pre‐calculated variable attenuation length of muons and accounts for temporal changes of the magnetic field. It also assumes a specific distribution of denudation rates throughout the basin and uses an iterative approach to calculate the basin average denudation rates. We tested the two approaches across several natural basins and found that both approaches provide similar denudation results. Hence, assuming exponential muogenic production and constant attenuation length of muons in the rock has little impact on the derived denudation rates. Therefore, unless a priori known distributions of denudation rates are to be tested, there does not appear to be any particular gain from using the second iterative method which is computationally less effective. Based on these findings, we developed and describe here Basinga , a new ArcGIS® and QGIS toolbox which computes the basin average scaling factors, cosmogenic production rates and denudation rates for several tens of drainage basins together. Basinga follows either the Lal/Stone or the Lifton/Sato/Dunai scaling schemes and includes several optional tools for correcting for topographic shielding, ice cover and lithology. We have also developed an original method for correcting the cosmogenic production rates for past variations in the Earth's magnetic field. © 2019 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".