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Record W2564715533 · doi:10.1139/as-2016-0039

The Greenland vascular plant herbarium of the University of Copenhagen

2016· article· en· W2564715533 on OpenAlexvenueno aff
Christian Bay, Fred J.A. Daniëls, G. Halliday

Bibliographic record

VenueArctic Science · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsHerbariumFlora (microbiology)Vascular plantArcticGeographyPhytogeographyVegetation (pathology)GroenlandiaShetlandPhysical geographyThe arcticOceanographyEcologyArchaeologyGeologyForestryIce sheetBiologyPaleontology

Abstract

fetched live from OpenAlex

By the establishment of the Greenland Botanical Survey in 1962 at the Botanical Museum, University of Copenhagen, an era of regular and systematic exploration of the vascular plant flora of Greenland was initiated and it ended in 1996, when funding ended. Preceding this period, the vascular plant flora was mainly known from the results of more sporadic botanical investigations mostly in low arctic West and East Greenland, but after the 1980s, investigations expanded to include the more inaccessible high arctic Northeast and North Greenland. Nowadays, vascular plant species have been collected from most regions of Greenland. So far, three regional phytogeographical studies of South, North, and West Greenland have been published, and at present, two papers dealing with the vascular plant flora of East Greenland are ready for publication. These studies will be the basis for a synopsis of the phytogeography of Greenland and a new edition of the Flora of Greenland. The published distribution maps from South, West, and North Greenland based on these collections have been digitized and used for modelling the regional vegetation and flora and its relation to past glaciations and current climate. The specimens from East Greenland have been entered into a database and will be available for future modelling projects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.189
Teacher spread0.168 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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