Temporal evolution of IP25 and other highly branched isoprenoid lipids in sea ice and the underlying water column during an Arctic melting season
Bibliographic record
Abstract
In recent years, certain mono- and di-unsaturated highly branched isoprenoid (HBI) alkene biomarkers (i.e., IP25 and HBI IIa) have emerged as useful proxies for sea ice in the Arctic and Antarctic, respectively. Despite the relatively large number of sea ice reconstructions based on IP25 and HBI IIa, considerably fewer studies have addressed HBI variability in sea ice or in the underlying water column during a spring bloom and ice melt season. In this study, we quantified IP25 and various other HBIs at high temporal and vertical resolution in sea ice and the underlying water column (suspended and sinking particulate organic matter) during a spring bloom/ice melt event in Baffin Bay (Canadian Arctic) as part of the Green Edge project. The IP25 data are largely consistent with those reported from some previous studies, but also highlight: (i) the short-term variability in its production in sea ice; (ii) the release of ice algae with high sinking rates following a switch in sea ice conditions from hyper- to hyposaline within the study period; and (iii) the occurrence of an under-ice phytoplankton bloom. Outcomes from change-point analysis conducted on chlorophyll a and IP25, together with estimates of the percentage of ice algal organic carbon in the water column, also support some previous investigations. The co-occurrence of other di- and tri-unsaturated HBIs (including the pelagic biomarker HBI III) in sea ice are likely to have originated from the diatom Berkeleya rutilans and/or the Pleurosigma and Rhizosolenia genera, residing either within the sea ice matrix or on its underside. Although a possible sea ice source for HBIs such as HBI III may also impact the use of such HBIs as pelagic counterparts to IP25 in the phytoplankton marker-IP25 index, we suggest that the impact is likely to be small based on HBI distribution data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".