MétaCan
Menu
Back to cohort
Record W4410634024 · doi:10.1038/s41467-025-59963-0

Bioavailable phosphite in the surface ocean during the Great Oxidation Event

2025· article· en· W4410634024 on OpenAlexaff
Abu Saeed Baidya, Joanne S. Boden, Yuhao Li, Albertus J.B. Smith, Kurt O. Konhauser, Eva E. Stüeken

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Alberta
FundersH2020 Marie Skłodowska-Curie ActionsNatural Environment Research CouncilEngineering and Physical Sciences Research CouncilRoyal SocietyUniversity of St AndrewsResearch Councils UKUK Research and Innovation
KeywordsPhosphateFerricPhosphorusSeawaterChemistryEnvironmental chemistryAdsorptionArcheanAbiogenesisInorganic chemistryGeologyOrganic chemistryGeochemistryOceanographyBiologyAstrobiology

Abstract

fetched live from OpenAlex

Abstract Phosphorus availability has influenced the co-evolution of life and Earth’s environments. While phosphate was likely the main phosphorus source for life during the Archean, phosphite (HPO 3 2 − ) gained importance leading up to the Great Oxidation Event (GOE). However, the concentration of phosphite in seawater at that time, and the processes driving this shift in P utilization, remain poorly constrained. Using lab experiments and phosphite data from banded iron formations (BIFs), we show that hydrous ferric oxides (HFO) preferentially remove phosphate over phosphite. This suggests that shallow seawater at the onset of the GOE could have contained up to 0.17 µM phosphite, comprising 5–88% of total dissolved inorganic phosphorus. We propose that phosphate depletion driven by HFO adsorption and microbial competition may have promoted the use of phosphite as an alternative P source.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.238
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicGroundwater and Isotope GeochemistryFrench-language works237,207