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Record W1973927603 · doi:10.1144/0016-764905-003

Latest Mid-Pennsylvanian tree-fern forests in retrograding coastal plain deposits, Sydney Mines Formation, Nova Scotia, Canada

2005· article· en· W1973927603 on OpenAlexaboutno aff
Howard J. Falcon‐Lang

Bibliographic record

VenueJournal of the Geological Society · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsPennsylvanianNova scotiaGeologyCoastal plainFernPaleontologyPaleozoicGeochemistryArchaeologyGeographyOceanographyEcology

Abstract

fetched live from OpenAlex

A latest Mid-Pennsylvanian (early Cantabrian) tree-fern forest is reported from Cape Breton Island, Nova Scotia. The fossil forest is rooted below the Point Aconi coal seam of the Sydney Mines Formation at Cranberry Head. The tree-ferns are preserved in the deposits of a coastal plain, subject to brackish incursions. They were constructed of psaronid-type trunks and massive pecopterid fronds, and locally intergrew with sigillarian lycopsids, neuropterid pteridosperms, and calamiteans. Basal stump diameter measurements, including the root mantle, range from 12 to 89 cm, indicating that the ferns were mostly large forest trees, rather than shrubs. Stump distribution measurements on palaeosols indicate that localized patches of tree-fern-dominated forest attained densities of c . 3850 trees per hectare when scaled up to standard forestry units, much denser than typical Mid-Pennsylvanian lycopsid forests. Tree-ferns dominantly grew in aggrading floodbasin settings, and a few of the largest trees show evidence for post-burial regeneration. Being approximately coeval with the late Mid-Pennsylvanian extinction event, which resulted in tree-fern forests rising to dominate tropical lowlands for the first time, the Cranberry Head fossil forests provide insight into community composition and structure during a critical phase of ecosystem reorganization.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.903
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.184
Teacher spread0.170 · 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.

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

Citations37
Published2005
Admission routes1
Has abstractyes

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