The application of Monte Carlo modelling to quantify in situ hydrogen and associated element production in the deep subsurface
Bibliographic record
Abstract
The subsurface production, accumulation, and cycling of hydrogen (H 2 ), and cogenetic elements such as sulfate (SO 4 2- ) and the noble gases (e.g., 4 He, 40 Ar) remains a critical area of research in the 21st century. Understanding how these elements generate, migrate, and accumulate is essential in terms of developing hydrogen as an alternative low-carbon energy source and as a basis for helium exploration which is urgently needed to meet global demand of this gas used in medical, industrial, and research fields. Beyond this, understanding the subsurface cycles of these compounds is key for investigating chemosynthetically-driven habitability models with relevance to the subsurface biosphere and the search for life beyond Earth. The challenge is that to evaluate each of these critical element cycles requires quantification and accurate estimates of production rates. The natural variability and intersectional nature of the critical parameters controlling production for different settings (local estimates), and for the planet as a whole (global estimates) are complex. To address this, we propose for the first time a Monte Carlo based approach which is capable of simultaneously incorporating both random and normally distributed ranges for all input parameters. This approach is capable of combining these through deterministic calculations to determine both the most probable production rates for these elements for any given system as well as defining upper and lowermost production rates as a function of probability and the most critical variables. This approach, which is applied to the Kidd Creek Observatory to demonstrate its efficacy, represents the next-generation of models which are needed to effectively incorporate the variability inherent to natural systems and to accurately model H 2 , 4 He, 40 Ar, SO 4 2- production on Earth and beyond.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 | 0.000 |
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 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".