Compost Cation Exchange Capacity via Portable X-Ray Fluorescence (PXRF) Spectrometry
Bibliographic record
Abstract
Compost is a valuable organic amendment which affords substantive fertility to soils where applied. A common component of compost fertility is cation exchange capacity (CEC), which has traditionally been determined via standard wet chemistry laboratory methods. This research utilized portable X-ray fluorescence (PXRF) spectrometry to evaluate 74 compost samples from the USA and Canada. PXRF elemental data were used for predicting compost CEC via random forest (RF) regression. Comparison between laboratory-determined vs. PXRF predicted CEC produced the following relationships: R2=0.90, RMSE = 5.41 meq 100 g−1 (model calibration) and R2=0.60, RMSE = 8.07 meq 100 g−1 (model validation). A key advantage of this technique is that the same data used for CEC prediction can also yield insight into other compost parameters of interest such as heavy metal content, plant essential nutrient content, salinity, and pH. Taken collectively, the PXRF approach can provide rapid, on-site analysis of compost which was previously not feasible with conventional methods. Our initial study has established the viability of PXRF for compost CEC determination, with further development on a wider array of feedstocks suggested for future study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".