Code for 1- and 2-stage oxygen fractionation and metamorphic dehydration modeling from "Seawater-oceanic crust interaction constrained by triple oxygen and hydrogen isotopes in rocks from the Saglek-Hebron Complex, NE Canada"
Why this work is in the frame
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Bibliographic record
Abstract
Current package comprises several files related to the publication Kutyrev, A., Bindeman, I. N., O'Neil, J. & Rizo, H. (2024). Seawater-oceanic crust interaction constrained by triple oxygen and hydrogen isotopes in rocks from the Saglek-Hebron complex, NE Canada: Implications for moderately low-δ18O Eoarchean Ocean. Chemical Geology 670. 10.1016/j.chemgeo.2024.122378 Below you will find the descriptions of each file with the brief instructions: Modelling of metamorphic dehydration (Oxygen_metam_dehydration.py) This code does not require any amendments – the average composition of Saglek-Hebron basalt is already there. To run the code, you need to have the file SD2_DBOXYGEN2.0.3 to be present in the same folder as .py file. This file is an Internally-consistent database for oxygen isotope fractionation between minerals from Vho et al. (2019). The results will be output in the console as saved as a pdf file in the same folder as the .py file. Vho, A., Lanari, P., and Rubatto, D., 2019, An internally-consistent database for oxygen isotope fractionation between minerals: Journal of Petrology, v. 60, no. 11, p. 2101-2129. 1-stage modelling.py To run this code, you should enter the values for the initial rock composition (d18Os_init, D17Os_init) and composition of reacting water (d18Ow_init, D17Ow_init). Subsequently, you should choose the output type on the line 13. If you print “basalt” (default), the output will be a figure with basalt oxygen isotope composition, if you choose water, the output will show the composition of water shifted during interaction with basalt. After the code is run, the results will be saved into the pdf file in the same folder as the .py file with the code. Equations used in the modelling were taken from Wostbrock, J. A. G., and Sharp, Z. D., 2021, Triple oxygen isotopes in silica–water and carbonate–water systems: Reviews in Mineralogy and Geochemistry, v. 86, no. 1, p. 367-400. Fractionation factors of basalt and sediments were calculated using the data from Schauble, E. A., and Young, E. D., 2021, Mass dependence of equilibrium oxygen isotope fractionation in carbonate, nitrate, oxide, perchlorate, phosphate, silicate, and sulfate minerals: Reviews in Mineralogy and Geochemistry, v. 86, no. 1, p. 137-178. 2-stage modelling.py To run this code, you also should enter the initial values for the initial rock composition (d18Os_init, D17Os_init) and composition of reacting water (d18Ow_init, D17Ow_init). After the code is run, the results (altered rock compositions) will be saved into the pdf file in the same folder as the .py file with the code. 1-stage Monte Carlo.py Here you need to choose the desired oxygen isotope compositional range of the altered basalt (lines 8 and 9). Also, you can change the number of iterations (line 12) and the oxygen isotope composition of the basalt before alteration (line 18, mantle values by default). After the code is run, the results will be saved into the pdf file in the same folder as the .py file with the code. 2-stage Monte Carlo.py To run 2-stage Monte Carlo you need to choose the desired oxygen isotope compositional range of the altered basalt (lines 10 and 11). Also, you can change the number of iterations (line 14) and the oxygen isotope composition of the basalt before alteration (line 24, mantle values by default). In addition, you need to select the rock that interacts with water on the 1-st stage (line 16, ‘basalt’ by default). The options available are: basalt, carbonate, quartz, metasediment, metasediment_high_si. After the code is run, the results will be saved into the pdf file in the same folder as the .py file with the code.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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