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Record W6962218636 · doi:10.1594/pangaea.869618

Modern pollen data from the Canadian Arctic (1972-1973)

2017· other· en· W6962218636 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPollenArcticTundraTaigaTransectVegetation (pathology)Moss

Abstract

fetched live from OpenAlex

This data descriptor reports results of a 1972–73 baseline study of modern pollen deposition in the Canadian Arctic to originally aid interpretation of Holocene pollen diagrams from that region, especially focussed on the arctic tree-line. The data set is geographically unique due to its extent, and allows the assessment of the effects of modern climate change on northern ecosystems, including fluctuations of the a arctic tree-line. Repeated sampling was conducted along an interior transect at 29 sites from the Boreal Forest to the High Arctic, with five additional coastal sites covering a total distance of 3,200 km. Static pollen samplers captured both local pollen and long-distance pollen wind-blown from the Boreal Forest. Moss and lichen polsters provided multi-year pollen fallout to assess the effectiveness of the static pollen samplers. The local vegetation was recorded at each site. This descriptor provides information on data archived at the World Data Center PANGAEA, which includes spreadsheets detailing site and sample information as well as raw and processed pollen data obtained on over 500 samples.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0080.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.8280.428

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 head0.224
GPT teacher head0.328
Teacher spread0.103 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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