4D printed deformation labels with machine learning for monitoring and preservation of respiring climacteric fruits
Bibliographic record
Abstract
4D printed labels that change color and shape were developed to achieve the dual functions of quality assessment and maintenance of respiring climacteric fruits. The effect of addition of essential oil emulsion and different geometric structures on deformation as well as the corresponding mechanisms were explored. Cast, 3D printed, and 4D printed labels were compared based on their responses to fruit quality and compatibility with machine learning. The addition of emulsion significantly affected the degree of deformation by altering the printing fidelity, hydrophilicity, and flexibility of the network structure. Geometric designs (Including printing layers, filament intersection angles, and infill ratios) changed both the direction and degree of deformation. Unlike cast and 3D printed labels, 4D printed labels simultaneously changed color and shape in response to variations in humidity and carbon dioxide levels in the package, enabling more accurate visual monitoring the turning points of fruit quality. The MobileNet model achieved recognition accuracy of 97% for 4D printed labels, which played an active role in achieving intelligent warnings. Additionally, deformation along with microstructure destruction positively impacted the controlled release of essential oils through a non-Fickian mechanism, resulting in a better preservation effect.
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 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".