Evaluation of a Spectrophotometric Method for Practical and Cost Effective Quantification of Fulvic Acid
Bibliographic record
Abstract
A large number of organic humic products are increasingly being applied worldwide especially in agricultural applications. Humic substances can be fractionated into three components: humic acid, fulvic acid and insoluble humin. A standard method based on acid precipitation is gaining acceptance for quantification of humic acid as the humic acid precipitates at pH < 2 and thus can be quantified by gravimetric measurements. However, the fulvic acid component remains in solution at all pH conditions and there is no practical and cost effective method available for measuring fulvic acid. This paper presents the results of our evaluation of spectrophotometric analysis of the fulvic acid content of commercial humic products. Based on the assumption that the optical properties of fulvic acids are independent of their sources, a calibration curve showing a linear relationship between varying concentrations of an IHSS standard fulvic acid and their UV/vis absorption at multiple wavelengths was established. The concentrations of fulvic acid in a variety of commercial products were then obtained by measuring the samples UV/vis absorption and applying the established calibration curve. The calculated fulvic acid concentrations were in reasonable agreement with the values obtained from carbon analysis of the test samples after corrections. Permanent URL: http://hdl.handle.net/2047/d10006060
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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".