Pleistocene-Holocene Deep Water Benthic Foraminifera, off lYiticorin Coast, Bay of Bengal
Bibliographic record
Abstract
Abstract Twenty-four Pleistocene-Holocene deepwater benthic foramimferal species were identified based upon taxonomic criteria from a 2 6 m core sample collected during Academik Aleksandr Sidorenko cruise The quantitative data of benthic foraminifera) taxa is treated statistically using multi vanate (both factor and cluster) techniques to understand the relationship between species assemblages and environmental parameters The temporal distribution of samples in cluster II closely coincides with the distribution of higher loading values of factor 1 Similar associations were identified for cluster III with factor 2 and cluster IV with factor 3 However the faunal relative abundances along with % Total Organic Content (TOC) values broadly divides the core into two environmentally significant zones viz, Zone 1 (between 1322 6m and 1321 6 m) which is characterised by high abundance of Cassiduhna cannata followed by Pullenia bulloides and Hoeglundina elegans having positive relationship with low TOC values ranging from 0 12 to 1 14 and Zone 2 (between 1321 6m and 1320 1 m), which is characterised by high percentages of TOC values ranging from 1 32 to 2 52, whereas Buhmina aculeata shows maximum abundance followed by Bohvina robusta, Cibiadoides kullenbergi and Osangulana cutter, suggesting that these species prefer high nutrient environment
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".