Minimum information for dielectric measurements of biological tissues (MINDER): A framework for repeatable and reusable data
Bibliographic record
Abstract
The dielectric properties of biological tissues characterise the interaction of human tissues with electromagnetic (EM) fields. Accurate knowledge of the dielectric properties of tissues are vital in EM-based therapeutic and diagnostic techniques, and for assessing the safety of wireless devices. Despite the importance of these properties, the field has suffered from inconsistencies in reported data. The dielectric measurement process for tissues is known to be affected by both measurement confounders and clinical confounders; however, adequate metadata is often lacking in the literature. For this reason, this work proposes a standard, called Minimum Information for Dielectric Measurements of Biological Tissues (MINDER). In the MINDER model, the minimum types of raw data and metadata needed to interpret or replicate a dielectric study are identified and described. Alongside the minimum information model, a controlled vocabulary for metadata parameters is proposed. We also provide an example of this model applied to a dielectric measurement scenario on a biological tissue sample. The MINDER model enables reproducibility of measurements, ease of interpreting and re-using data, and comparison of data across studies. Further, this standard framework will support dielectric databases, with data searchable through metadata parameters such as temperature, frequency range, tissue type, and tissue state.
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 | 0.048 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".