Sedimentary indicators of anthropogenic impact in Fildes Peninsula lakes (King George Island, Maritime Antarctica)
Bibliographic record
Abstract
Fildes Peninsula, on King George Island, is among the Antarctic sites with the most intense human activity and is located in a region strongly influenced by global warming. While alterations to its once pristine environments have been noted, there is a lack of data concerning the region’s natural state before the increased human presence (∼1968). We studied seven lakes from Fildes Peninsula to assess anthropogenic effects on their ecological processes by studying pre- and post-anthropic sediments with a top-bottom approach. We examined differences in bacterial and phytoplankton communities using 16S rRNA metabarcoding, HPLC (high performance liquid chromatography) pigments and analysis of sediment metals. We observed lake-specific differences in bacterial communities between pre- and post-anthropic samples. Using indicator species analysis, we identified bacteria associated with polluted environments (e.g., KD4–96, Bacteroidetes vadinHA17, Hungateiclostridiaceae and Leptolinea ) in post-anthropic sediments from two lakes that showed notable increases of metals. As both lakes are very close to roads and airport infrastructure, these associations may imply the greater recent presence of compounds including petroleum derivatives. Results indicated good preservation of bacterial DNA, but also that diagenetic processes may have affected pigment concentrations. Our data suggest that bacterial DNA may be used as a sedimentary proxy to reconstruct environmental changes including anthropogenic impacts in Antarctic lakes. • Bacterial sedimentary DNA was used to assess human impacts in Antarctic lakes. • Metal enrichment occurred in lakes that were close to transportation infrastructure. • Pollution-resistant bacteria were more abundant in recent samples in impacted lakes.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".