Paleoceanographic interpretation of the light rare earth elements
Bibliographic record
Abstract
and 10 5 years; fossil fish teeth and debris pinpoint water mass changes through the Cenozoic, though also with low temporal resolution due to the scarcity of fish remains [2; 3]. Records of much higher temporal resolution have been produced from the eNd of bulk sediment leach, and over glacial-interglacial timescales [4]. Isolating the eNd of seawater from lithogenic contamination, however, is problematic. Detrital contaminants are much more easily removed from planktonic foraminifera and therefore may be more representative of a seawater signal [1]. The phase in which the eNd is associated with foraminiferal calcite is of critical importance for the paleoceanographic interpretation. In addition to Nd, the full sequence of rare earth elements can speak to diagenetic indicators and bottom-water conditions in which the authigenic signal is acquired. Studies of coupled planktonic benthic REE comparison allow for clear indications of diagenetic overprinting. Here, we present data from the Iberian Margin that demonstrate coherent, diagenetic signals indicative of environmental conditions associated with climate signals. Also, we present data generated using X-ray absorption near edge structure analysis that help to identify finescale spatial distribution of high concentrations of REE and also the oxidation state of redox sensitive cerium. With a combination of paleorecords and different analytical approaches, we can begin to identify the distinct phases of the REE and as a result, the paleoceanographic interpretation of these elements.
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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 | 0.002 | 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) | 0.008 | 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".