Advanced Residuals Analysis for Determining the Number of PARAFAC Components in Dissolved Organic Matter
Bibliographic record
Abstract
Parallel factor analysis (PARAFAC) has facilitated an explosion in research connecting the fluorescence properties of dissolved organic matter (DOM) to its functions and biogeochemical cycling in natural and engineered systems. However, the validation of robust PARAFAC models using split-half analysis requires an oft unrealistically large number (hundreds to thousands) of excitation-emission matrices (EEMs), and models with too few components may not adequately describe differences between DOM. This study used self-organizing maps (SOM) and comparing changes in residuals with the effects of adding components to estimate the number of PARAFAC components in DOM from two data sets: MS (110 EEMs from nine leaf leachates and headwaters) and LR (64 EEMs from the Lena River). Clustering by SOM demonstrated that peaks clearly persisted in model residuals after validation by split-half analysis. Plotting the changes to residuals was an effective method for visualizing the removal of fluorophore-like fluorescence caused by increasing the number of PARAFAC components. Extracting additional PARAFAC components via residuals analysis increased the proportion of correctly identified size-fractionated leaf leachates from 56.0 ± 0.8 to 75.2 ± 0.9%, and from 51.7 ± 1.4 to 92.9 ± 0.0% for whole leachates. Model overfitting was assessed by considering the correlations between components, and their distributions amongst samples. Advanced residuals analysis improved the ability of PARAFAC to resolve the variation in DOM fluorescence, and presents an enhanced validation approach for assessing the number of components that can be used to supplement the potentially misleading results of split-half analysis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".