Data for "Determining Strain Components in a Diamond Waveguide from Zero-Field ODMR Spectra of NV- Center Ensembles"
Bibliographic record
Abstract
Data repository for: Determining Strain Components in a Diamond Waveguide from Zero-Field ODMR Spectra of NV- Center Ensembles ‘Raw_data.txt' contains the data as collected from ODMR experiments. The first column represents the microwave frequency, while the subsequent columns contain counts for positions 1 to 47, as detailed in the Supplementary Material (Sec II). 'Preprocessed_data.txt' includes the preprocessed data, as described in both the main text (Sec II.B) and the Supplementary Material (Sec II). The format of this data is consistent with that described above. Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ITN project LasIonDef (GA no. 956387). MG acknowledges support from the National Science Centre (Poland) under Grant No. 2015/18/E/ST3/00583. DW acknowledges financial support by the Science Foundation Ireland (SFI) under grants nos. 18/RP/16190 and 22/PATH-S/10656. AJB and JPH acknowledge the financial support provided by EPSRC via Grant No. EP/T017813/1 and EP/03982X/1. VB acknowledges the support of the Alexander von Humboldt Foundation. AB gratefully acknowledges financial contribution from MAECI, "Italy-Israel joint programme - 2023 scientific track" within the project PRECIOUSMRI. SME is thankful for the support from the projects QuantDia (FISR2019-05178) and PNRR PE0000023 NQSTI funded by MUR (Ministero dell'Università e della Ricerca). MG, DW, and PM acknowledge support from the Alexander von Humboldt Foundation in the framework of the Research Group Linkage Programme funded by the German Federal Ministry of Education and Research.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.232 | 0.088 |
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".