(Invited) Plant Wearable for Enhanced Agricultural Productivity
Bibliographic record
Abstract
With increased global population, it is important to ensure their basic need: food. While eighty percent of the world's population depend on five agricultural products: rice, wheat, corn, barley, sugar cane, only in 20% areas they can be grown. Often, due to uncertain climatic condition, soil condition, excessive rain, and other natural uncertainties, the agricultural productivity is compromised and often not optimally controlled. Although, with digitization, this unfortunate situation is changing, due to high price, need for constant seamless connectivity and precision and fast data analytics, such digitization is not proliferating at the rate it is required. Finally, naturally most plants have different "skins", shapes, sizes and sensitivities. Here we show, low-cost, standalone, seamlessly connected plant wearable which can conform to sensitive plant sides and can monitor the surrounding micro-climate and plants botanical condition. We use heterogeneous integration of hybrid materials, devices and processes to achieve such ultra-light weight and low-cost disposable systems which potentially change our perspective about traditional ICs and their integration in our daily life. Figure 1
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".