3-D Structural Modeling of Humic Acids through Experimental Characterization, Computer Assisted Structure Elucidation and Atomistic Simulations. 1. Chelsea Soil Humic Acid
Bibliographic record
Abstract
This paper describes an integrated experimental and computational framework for developing 3-D structural models for humic acids (HAs). This approach combines experimental characterization, computer assisted structure elucidation (CASE), and atomistic simulations to generate all 3-D structural models or a representative sample of these models consistent with the analytical data and bulk thermodynamic/structural properties of HAs. To illustrate this methodology, structural data derived from elemental analysis, diffuse reflectance FT-IR spectroscopy, 1-D/2-D 1H and 13C solution NMR spectroscopy, and electrospray ionization quadrupole time-of-flight mass spectrometry (ESI QqTOF MS) are employed as input to the CASE program SIGNATURE to generate all 3-D structural models for Chelsea soil humic acid (HA). These models are subsequently used as starting 3-D structures to carry out constant temperature-constant pressure molecular dynamics simulations to estimate their bulk densities and Hildebrand solubility parameters. Surprisingly, only a few model isomers are found to exhibit molecular compositions and bulk thermodynamic properties consistent with the experimental data. The simulated 13C NMR spectrum of an equimolar mixture of these model isomers compares favorably with the measured spectrum of Chelsea soil HA.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".