Binding of RDX to Cell Wall Components of<i>Pinus sylvestris</i>and<i>Picea glauca</i>and Three-Year Mineralisation Study of Tissue-Associated RDX Residues
Bibliographic record
Abstract
Contamination of soils with the explosive hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX, Research Department Explosive) as a result of military applications is a large-area problem globally. Since coniferous trees dominate the vegetation of large areas of military land in Central Europe, particularly in Germany, the long-term fate of (14)C-RDX in the conifers Scots pine and Dwarf Alberta spruce was studied. Acetic acid was the most effective solvent for the removal of extractable RDX residues from homogenates of RDX-laden tree material (85%, 80-90% and 64-80% for roots, wood and needles, respectively). On average, only a fifth of RDX-derived (14)C was bound in non-extractable residues (NER). Within the main cell wall compartments, lignin was the dominant binding site for NER (needles: 32-62%; roots: 38-42%). Hemicellulose (needles: 11-18%; roots: 6-11%) and cellulose (needles: 12-24%; roots: 1-2%) were less involved in binding and a considerable proportion of NER (needles: 15-24%; roots: 59-51%) was indigestible. After three-year incubation in rot chambers, mineralisation of tree-associated (14)C-RDX to (14)CO2 clearly dominated the mass balance in both tree species with 48-83%. 13-33% of (14)C-RDX-derived radioactivity remained in an unleachable form and the remobilisation by water leaching was negligible (< 2%).
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".