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Record W6948201312 · doi:10.5061/dryad.h70rxwdwg

Data from: The earliest known fungal-induced biomineralization in fossil bones, and its role in the marine ecosystem

2025· dataset· en· W6948201312 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2025
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsBiomineralizationBiogeochemical cycleFossil RecordFluoriteTaphonomyMarine ecosystemTufaEcosystem

Abstract

fetched live from OpenAlex

Formation of microtubes, defined as small internal borings, in fossil and modern bone is a well-attested phenomenon. However, determining whether microtubes were created by microbial activity or abiotic processes is challenging, particularly in fossils. Here, we report abundant microtubes in compact bone from numerous specimens of the marine reptile Keichousaurus from the Middle Triassic of southwestern China. Light and scanning electron microscope imaging of osteological thin sections, and CT-based 3D reconstruction of the microtubes, reveal geometric features typical of fungal hyphae, such as bifurcation and tight helical coiling. Some microtubes contain what may be the first known fossilized fungal vacuoles. The microtubes are thus likely to be of fungal origin, produced by saprobic marine fungi during decomposition. Furthermore, fluorine is abundant in the compact bone, and even more prevalent in the infillings that occur in many microtubes. The fungi evidently released calcium ions and took up fluorine from the reptiles’ bodies, and promoted the formation of fluorite in the microtubes. The infillings represent the earliest known instance of fungal-induced biomineralization within fossil bone, demonstrating that some Middle Triassic fungi were capable of impacting global biogeochemical cycling by taking up substantial amounts of fluorine.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.379
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0440.001

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.

Opus teacher head0.077
GPT teacher head0.311
Teacher spread0.234 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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