Does the Coralline Alga <i>Leptophytum foecundum</i> (Kjellman) Capture Paleoenvironmental Variability in the Arctic Ocean?
Bibliographic record
Abstract
Records of high resolution climate variability in the past are essential to understanding the climate change observed today. This is particularly true for Arctic regions, which are rapidly warming. Prior to instrumental data, proxy records can be extracted from high-latitude climate archives to provide critical records of past Arctic climate variability. Here, we investigate the feasibility of extracting records of climate and environmental variability from the skeleton of the crustose coralline alga Leptophytum foecundum from offshore the Sagavanirktok River in the Beaufort Sea. Although this alga forms an annually banded skeleton, age chronologies were established with difficulty due to the large uncalcified reproductive structures relative to low annual growth rates. Average measurements of skeletal Mg content, δ18Oalga values, and δ13Calga values were consistent among the analyzed specimens, but time series of these parameters only significantly correlated between two of the collected specimens for δ18Oalga. No clear trends in environmental variability explained the patterns in the skeletal geochemistry over time. This suggests that ambient seawater combined with freshwater from the Sagavanirktok River drives the geochemistry of L. foecundum at this site. Thus, coralline algal specimens located near variable sources of low-salinity waters are not ideal organisms to use as proxy archives.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".