LLSM Dataset for Virtual and Augmented reality
Bibliographic record
Abstract
LLSM Dataset for Virtual and Augmented Reality. This dataset includes data generated by Lattice Light Sheet Microscopy. Image metadata is described in the file: FilesDescription_LLSM_Datasets_VR_AR.xlsx. This dataset includes: Rab5(early endosomes), Plasma Membrane CellMask (endosomal pathway) and CD63-Turquoise.2 (late-multivesicular endosomes). Lattice light-sheet microscopy. LLSM was done on a commercialized version of a previously described setup4 from 3i (Denver, USA). Cells were scanned incrementally through a 20 μm long light sheet in 500 nm steps using a fast piezoelectric flexure stage equivalent to ∼271 nm or 600 nm steps using a fast piezoelectric flexure stage equivalent to ∼325 nm, with respect to the detection objective and were imaged using a sCMOS camera (Orca-Flash 4.0; Hamamatsu, Bridgewater, NJ). Excitation was achieved with 488- (Coherent, Santa Clara, CA), 560- or 642-nm diode lasers (MPB Communications, Canada) through an excitation objective (Special Optics 28.6× 0.7 NA 3.74-mm water-dipping lens) and detected via a Nikon CFI Apo LWD 25× 1.1 NA water-dipping objective with a 2.5× tube lens with a final pixel size of 104 nm. Lattice light-sheet imaging was performed using an excitation pattern of outer NA equal to 0.55 and inner NA equal to 0.493. Acquired data were deskewed, a necessary step to realigned image frames, then deconvolved using LLSpy (https://github.com/tlambert03/LLSpy).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.096 |
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".