Fine‐scale chemostratigraphy of cross‐sectioned hydrogenous ferromanganese nodules from the western North Pacific
Bibliographic record
Abstract
Abstract A broad area densely covered by ferromanganese nodules was recently discovered around Minamitorishima (Marcus) Island, representing a high‐potential metal resource, particularly for Co, Ni, Mo, and W. We studied 16 nodule samples from nodule fields around Minamitorishima Island. To define the fine‐scale chemostratigraphy of the nodules, polished cross‐sections of the samples were analyzed by microfocus X‐ray fluorescence. Our results show that a general pattern of compositional variation was common throughout the growth history of the nodules in all the regions we studied. Chemical mapping clarified changes in the chemical signature and proportion of five lithological components throughout the growth history: Mn represented columnar δ ‐MnO 2 ; Fe represented layered amorphous FeOOH*xH 2 O; Ti represented TiO 2 *2H 2 O intergrown with an amorphous FeOOH phase; P, Ca and Y represented particles of biogenic calcium phosphate; and Si, Al, K, Cu, and Ni represented pelagic sediment infills. We proposed a method for a creating a multi‐dimensional compositional map of the fine‐scale chemostratigraphy observed in the ferromanganese oxide layers on the basis of merging the mapped Mn, Fe, Ti, P, Si and Cu intensities. Multi‐dimensional compositional mapping of the sampled nodules from the western North Pacific revealed two fundamental findings: (1) previously recognized first‐order Fe–Mn layers, L0, L1, and L2, were further divided into two, three, and four sublayers, respectively, and (2) a delayed supply of material to be nuclei of nodule or a growth hiatus of Fe–Mn layer(s), leading to missing sublayers in the layers L0 and L2, regulated the nodule size. In contrast, layer L1, which does not have any missing sublayers, was commonly observed in the samples for this study and has been reported in studies of other regions in the western Pacific. We propose, therefore, that the layer L1 is a key facies for examining chemostratigraphic correlations with other areas of seafloor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".