Paleozoic multiple accretionary and collisional processes of the Beishan orogenic collage
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
The dissolved chemistry of rivers has been extensively studied to elucidate physical and climatic controls of chemical weathering at local to global spatial scales, as well as the impacts of chemical weathering on climate over short to geologic temporal scales. Within this effort, mixing models with Monte Carlo uncertainty propagation are a common tool for inverting measurements of dissolved river chemistry to distinguish among contributions from end-members with distinct elemental and/or isotopic compositions. However, the methods underlying prior river inversion models have typically been opaque. Here we present Mixing Elements ANd Dissolved Isotopes in Rivers (MEANDIR), a set of MATLAB scripts that enable highly customizable inversion of dissolved river chemistry with Monte Carlo propagation of uncertainty. First, we present an overview of the mathematics underlying MEANDIR. This overview includes, among other topics, derivation of equations for mass balance, implementation of chlorine critical values, construction of cost functions, normalization to the sum of dissolved variables, quantification of river sulfate sourced from pyrite oxidation, resolution of petrogenic organic carbon oxidation, representation of secondary phase formation with isotopic fractionation, and calculation of the impact of weathering on atmospheric carbon dioxide. Second, we apply MEANDIR to five previously published datasets to demonstrate the sensitivity of results to parameter choices. We invert data from two global compilations of river chemistry (Gaillardet and others, 1999; Burke and others, 2018), the major element chemistry and sulfate sulfur isotope ratios of rivers in the Peruvian Amazon (Torres and others, 2016), the major element chemistry of Icelandic rivers (Gíslason and others, 1996), and the major and trace element chemistry of water samples from the Mackenzie River (Horan and others, 2019). MEANDIR and its user guide are freely available online.
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 | 0.000 | 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 | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it