A novel, solar panel-based screenwashing apparatus for the bulk collection of microvertebrate fossils in the field
Bibliographic record
Abstract
The contribution of microvertebrate fossils towards various paleobiological and geochemical studies are becoming increasingly more numerous and significant. As such, several methods have been developed for the extraction and collection of microfossils from bulk sediment. In the field, screenwashing relies on a sieve in a fluvial setting to passively wet sieve the fossiliferous sediment. Sampling in the field can be much easier as it alleviates the need to transport a large quantity of bulk sediment back to the home institution. However, the primary concerns of sampling bulk matrix in the field are access to a fluvial amenity and availability of sediment that can be successfully wet sieved. We encountered both issues at a locality where: 1) there was no access to any sort of fluvial feature, and 2) even with a man-made water containing feature, the clay-rich sediment at this locality created an impermeable layer in each screen box that clogged the screen, and prevented wet sieving. To overcome these challenges, we designed and implemented a two-part apparatus onsite in the field that relied on a solar-powered water transfer pump to cycle water throughout a system to provide fluvial agitation; whereby preventing the buildup of an impermeable clay layer, and allowing the processing and collection of microvertebrate material from this locality in the field. While there are numerous protocols and methodologies for the processing of microvertebrate material, the methodology we document in this study highlights another technique that can be utilized, and will hopefully prove useful to others encountering similar difficulties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".