Applications of confocal macroscope-microscope luminescence imaging to sediment cores
Bibliographic record
Abstract
Abstract We demonstrate the successful application of a novel, confocal scanning laser macroscope-microscope (cslM/m) system for non-invasive imaging of samples taken from lake and ocean sediment cores. Advantages of the macroscope-microscope system over other macroscopic luminescence imaging techniques, such as scanning electron microscopy-based cathodoluminescence and scanning-stage laser imaging, are highlighted and the implications for new core analysis techniques are explored. The macroscope-microscope can image specimens ranging from 25 × 25 μm up to 7.5 × 7.5 cm in under 10 s using reflected light or photoluminescence as contrast mechanisms. Macroscope mode is used to rapidly survey the specimen and provide a photoluminescence ‘roadmap’. Microscope mode is used to provide ultra-high-resolution images of microfossils or areas of interest. Laser scanning is non-invasive and does not require any preparation of the specimen. Photoluminescence and fluorescence imaging results are shown for an entire core section recovered from Lake Huron by the Geological Survey of Canada (GSC). Photoluminescence images are shown for Ocean Drilling Program samples of a diatom mat and a radiolarian microfossil within the sample, a laminated interval of sediment from the Santa Barbara Basin (Site 893) and specimens from the Scotian Shelf (collected by the GSC).
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 | 0.000 | 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) | 0.002 | 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 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".