Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Noni seeds have been used for years as an important medicinal source, with wide use in the pharmaceutical and food industry. Drying is a fundamental process in the post-harvest stages, where it enables the safe storage of the product. Therefore, the present study aimed to fit different mathematical models to experimental data of drying kinetics of noni seeds, determine the effective diffusion coefficient and obtain the activation energy for the process during drying under different conditions of air temperature. The experiment used noni seeds with initial moisture content of 0.46 (decimal, d.b.) and dehydrated up to equilibrium moisture content. Drying was conducted under different controlled conditions of temperature, 40; 50; 60; 70 and 80 ºC and relative humidity, 24.4; 16.0; 9.9; 5.7 and 3.3%, respectively. Eleven mathematical models were fitted to the experimental data. The parameters to evaluate the fitting of the mathematical models were mean relative error (P), mean estimated error (SE), coefficient of determination (R2), Chi-square test (c2), Akaike Information Criterion (AIC) and Schwarz’s Bayesian Information Criterion (BIC). Considering the fitting criteria, the model Two Terms was selected to describe the drying kinetics of noni seeds. Effective diffusion coefficient ranged from 8.70 to 23.71 × 10-10 m2 s-1 and its relationship with drying temperature can be described by the Arrhenius equation. The activation energy for noni seeds drying was 24.20 kJ mol-1 for the studied temperature range.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it