Calibration of the carbon isotope composition (δ<sup>13</sup>C) of benthic foraminifera
Bibliographic record
Abstract
Abstract The carbon isotope composition (δ13C) of seawater provides valuable insight on ocean circulation, air‐sea exchange, the biological pump, and the global carbon cycle and is reflected by the δ13C of foraminifera tests. Here more than 1700 δ13C observations of the benthic foraminifera genus Cibicides from late Holocene sediments (δ13CCibnat) are compiled and compared with newly updated estimates of the natural (preindustrial) water column δ13C of dissolved inorganic carbon (δ13CDICnat) as part of the international Ocean Circulation and Carbon Cycling (OC3) project. Using selection criteria based on the spatial distance between samples, we find high correlation between δ13CCibnat and δ13CDICnat, confirming earlier work. Regression analyses indicate significant carbonate ion (−2.6 ± 0.4) × 10−3‰/(μmol kg−1) [CO32−] and pressure (−4.9 ± 1.7) × 10−5‰ m−1 (depth) effects, which we use to propose a new global calibration for predicting δ13CDICnat from δ13CCibnat. This calibration is shown to remove some systematic regional biases and decrease errors compared with the one‐to‐one relationship (δ13CDICnat = δ13CCibnat). However, these effects and the error reductions are relatively small, which suggests that most conclusions from previous studies using a one‐to‐one relationship remain robust. The remaining standard error of the regression is generally σ ≅ 0.25‰, with larger values found in the southeast Atlantic and Antarctic (σ ≅ 0.4‰) and for species other than Cibicides wuellerstorfi. Discussion of species effects and possible sources of the remaining errors may aid future attempts to improve the use of the benthic δ13C record.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".