Silicon isotopes in Antarctic sponges: an interlaboratory comparison
Bibliographic record
Abstract
Abstract Cycling of deepwater silicon (Si) within the Southern Ocean, and its transport into other ocean basins, may be an important player in the uptake of atmospheric carbon, and global climate. Recent work has shown that the Si isotope (denoted by δ 29 Si or δ 30 Si) composition of deep sea sponges reflects the availability of dissolved Si during growth, and is a potential proxy for past deep and intermediate water silicic acid concentrations. As with any geochemical tool, it is essential to ensure analytical precision and accuracy, and consistency between methodologies and laboratories. Analytical bias may exist between laboratories, and sponge material may have matrix effects leading to offsets between samples and standards. Here, we report an interlaboratory evaluation of Si isotopes in Antarctic and sub-Antarctic sponges. We review independent methods for measuring Si isotopes in sponge spicules. Our results show that separate subsamples of non-homogenized sponges measured by three methods yield isotopic values within analytical error for over 80% of specimens. The relationship between δ 29 Si and δ 30 Si in sponges is consistent with kinetic fractionation during biomineralization. Sponge Si isotope analyses show potential as palaeoceaongraphic archives, and we suggest Southern Ocean sponge material would form a useful additional reference standard for future spicule analyses.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".