Zooplankters in an oligotrophic ocean: contrasts in the niches of <i>Globigerinoides ruber</i> and <i>Trilobatus sacculifer</i> (Foraminifera: Globigerinida) in the South Pacific
Bibliographic record
Abstract
Distributions of the planktonic foraminifers Globigerinoides ruber (d’Orbigny) and Trilobus sacculifer (Brady) from the tropical-subtropical South Pacific Ocean are related to sea surface temperature (SST), chlorophyll-a, nitrate, phosphate, salinity and oxygen to determine whether their niches overlap. Their distributions in the ForCenS database of species in seafloor sediment are studied as proxies for upper ocean data. In the occurrence analysis (MaxEnt) SST is the strongest predictor of niche suitability followed by chlorophyll-a; environments between 0–20° S are the most suitable for both species: niches are undifferentiated. Contrarily, abundance analysis (Random Forests) identifies nitrate and chlorophyll-a as primary variables for Globigerinoides ruber, and SST and chlorophyll-a for Trilobatus sacculifer. Maximum abundances of the former are predicted in the subtropical hyper-oligotrophic zone while maxima of the latter are predicted at its margins and near the West Pacific Warm Pool. The high relative abundance of Globigerinoides ruber in the hyper-oligotrophic zone is attributed to its photosymbiotic relation with on-board dinoflagellates; this compensates for the low primary productivity in the zone. It is the best-adapted planktonic foraminifer in this huge marine ‘desert’ and is a proxy for hyper-oligotrophic environments. The photosymbiotic relation is weaker in Trilobatus sacculifer which primarily depends on particulate nutrition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".