Prediction of Cu(II) biosorption performances on wild mushrooms <i>Lactarius piperatus</i> using Artificial Neural Networks (ANN) model
Bibliographic record
Abstract
Abstract This work investigates the possible usage of edible mushrooms as support for metabolic quantities of copper. Biosorption potential of natural and biodegradable matrix formed from wild Lactarius piperatus mushrooms, in suspension (LP) and alginate immobilized based beads (LPAB), was explored. The effects of biomass quantity, Cu(II) concentration, and temperature were assessed. LPAB showed better adsorption capacity (7.67 mg/g) by comparison to LP biosorbent (6.43 mg/g). Also, biosorption efficiencies up to 76 and 99 % for LP and LPAB (for the same quantity of biomass, 2 g), respectively, were obtained. Furthermore, a multilayer feed forward Artificial Neural Network (ANN) model was developed in order to predict the biosorption efficiency. The trained ANNs, for LP and LAPB biosorbents, showed good correlation (R = 0.998) between the predicted and experimental biosorption efficiency, associated to reduced mean relative errors and demonstrated that the ANN has a good generalization potential. 1–2 g of Lactarius piperatus mushroom, as powder or in alginate‐based beads containing Cu(II), could be used as a dietary supplement in order to supply the daily copper demand of the organism.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".