Bibliographic record
Abstract
High-resolution digital photography and acquisition equipment is becoming widely available and cost-effective. Additionally, SEM and related acquisition technologies provide the means for obtaining images with resolution containing both structural and elemental information. Despite the development of new acquisition techniques and hardware, selecting an appropriate imaging solution for a research problem remains a critical issue for sedimentary research. Moreover, despite the differences between acquisition technologies, all share common approaches and considerations in order to obtain the highest quality images for quantitative sedimentary analysis. Consideration should be made to account for artefacts in the acquisition process, particularly that sample shape and illumination variations are accounted for. Additionally, color or equivalent image standards should be included in each image to permit quantifying the properties between images. Finally, image resolution and file storage protocols should be appropriate for the research objective and preserve the original image information (i.e., no compression). Technological advances will undoubtedly create new image acquisition opportunities for sedimentary researchers. Additionally, evolving approaches for the automation of image acquisition will provide increasingly efficient methods to obtain data from long sedimentary sequences or with higher spatial resolution.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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; both teacher heads agree on what is shown here.
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".