Scientific abstracts from the 7th International Barcode of Life Conference / Résumés scientifiques du 7<sup>e</sup> Conférence internationale « Barcode of Life »
Bibliographic record
Abstract
Background: Glaciers can be viewed as the most complete climate and environment archives, now severely threatened by climate change. These threats are particularly dramatic across the European Alps. The Adamello glacier is the largest, 16.4 km2, and deepest, 270 m, Italian glacier. We aim at estimating biodiversity changes over the last centuries in relation to climate and human activities in the Adamello catchment area by introducing a new approach: DNA metabarcoding of ice cores. Results: Pilot drilling was conducted in March 2015: the resulting 5 m core has been analysed in terms of pollen spectrum, stable isotopes, and ions to determine the stratigraphy. The results showed that a stratigraphy is evident: this 5 m ice core is corresponding to 5 years. DNA has been successfully extracted and amplified with specific barcodes: trnL cpDNA (primers d-h, about 150 bp) and a fragment of the mitochondrial COX1 (using three primer sets targeting the same region) have been used for investigating anemophilous plants and arthropod communities, respectively. Six libraries have been set up from three summer and three winter sections of the ice core. Plant metabarcoding not only confirms results obtained by morphological analysis but also demonstrates that ice cores provide a valuable source of eDNA, which allows identifications at species level. While most of the DNA is supposed to arise from pollen, in principle other material such as leaves might contribute to the total amount of DNA. Arthropod communities are mostly dominated by spiders, collembolans, and insects, the latter represented by dipteran species. Significance: The good preservation of eDNA in ice cores and the clear stratigraphy offers a unique opportunity to fully exploit the promise of metabarcoding for assessing how biodiversity has changed through time in particularly sensitive areas of the planet in relation to the effects of climate change.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.228 | 0.173 |
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".