Exploring the use of diatoms as a new environmental proxy in Arctic coastal ice cores - A first case study using the RECAP ice core
Bibliographic record
Abstract
We have recorded consistent (but low) numbers and a diverse range of diatom taxa (siliceous algae) over a 400-year period in the RECAP ice core, drilled from the Renland ice cap on the east coast of Greenland. This is an exciting initial step in attempting a diatom-based environmental reconstruction for an Arctic ice core for the first time, since Greenland’s inland ice cores (e.g. NGRIP, GRIP) do not appear to contain diatoms in enough numbers. Our novel study investigated the period 1528 - 1940 AD (encompassing the Little Ice Age (LIA)) and we developed a method for extracting diatom taxa from the ice-core meltwater samples for identification. This was done by microscopy using standard taxonomic techniques. In summary, the RECAP LIA assemblage comprises 93 species, 36 genera and 11 families where Thalassiosira/Coscinodiscus, Aulocoseira, Pinnularia, Nitzschia, Luticola, Diadesmis, Staurosira, Achnanthidium, Psammothidium spp are the dominant genera. In this interval we found that Renland received air blown diatoms from both planktonic/benthic freshwater (80%) and planktonic marine (20%) sources. The freshwater species included aerophilic species (from damp environments), key indicators of exposed, environments and found widely in the Arctic. We observe that both total diatom numbers and species composition changes rapidly over time (i.e. decadal timescales), similar to other ice-core proxies, and with higher total numbers/yr between about 1780 and 1850 AD. Further analysis is required to establish a link to specific environmental variables, which could include aridity, wind strength or sea ice cover. We hypothesise that similar lower altitude, coastal ice cores from Greenland and Canada could be useful diatom repositories in the Arctic region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".