Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditionally, photothermal spectroscopy (PTS) has been exploited for the detection of nanoparticles [2, 3]. With the advancement of broadband quantum cascade lasers (QCLs), optical photothermal infrared spectroscopy (O-PTIR) has gained interest for imaging applications due to its capability to combine IR specificity and submicron optical resolution [4, 5]. However, little has been reported on imaging artifacts. Based on a commercial confocal microscope, a MIR PTS instrument was developed at TU Wien to investigate photothermal image quality. Beside edge effects [1], standing wave patterns were observed in a thin-film sample. This phenomenon will be explained by investigating the effect of the detected signal on the photothermal image. [1] Aamont, L.C. and Murphy, J.C. (1982) “Effect of 3-D heat flow near edges in photothermal measurements.” Applied Optics 21(1):111-115. [2] Adhikari, S., Spaeth, P., Kar, A., Baaske, M. D., Khatua, S. and and Orrit, M. (2020) “Photothermal Microscopy: Imaging the Optical Absorption of Single Nanoparticles and Single Molecules”. ACS Nano 14:16414–16445. [3] Berciaud, S., Lasne, D., Blab, G. A., Cognet, L. and Lounis, B. (2006) “Photothermal heterodyne imaging of individual metallic nanoparticles: Theory versus experiment”. Physical Review B 73(4):045424. [4] Furstenberg, R., Crocombe, R. A., Kendziora, C. A., Papantonakis, M. R., Nguyen, V. and McGill, R. A. (2012) “Chemical imaging using infrared photothermal microspectroscopy”. Proc. of SPIE 8374.837411. [5] Zhang, D., Li, C., Zhang, C., Slipchenko, M. N., Eakins, G. and Cheng, J.-X. (2016) “Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution”. Sci. Adv. 2(9):e1600521.
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.
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 it