Bibliographic record
Abstract
Traditionally, studies on soil organic matter (SOM) begin with the extraction of SOM from soils, its fractionation into humic acid, fulvic acid, and humin, followed by de-ashing of each fraction. These are tedious, laborious and inefficient procedures that do not provide any chemical information on these materials. Instead, recently developed methods such as solid-state 13 C NMR and pyrolysis – field ionization mass spectrometry (Py-FIMS) can now be used for the in situ analysis of SOM in soils. These methods identify the major chemical components of SOM without extractions and fractionations, and yield valuable information on the main chemical structures in these materials. A better knowledge of the structural chemistry of SOM will help SOM chemists and other soil scientists to better understand the complex chemical and biochemical reactions that occur in soils, and will enable them to develop practices that will improve soil management and soil productivity. Key words: Extraction, fractionation, solid state 13 C NMR, pyrolysis-field ionization mass spectrometry, chemical composition
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.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".