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Record W6963097172 · doi:10.18739/a2v11vm5g

Holocene landscape change at the northern Manitoba tundra-forest border 2003-2009

2021· dataset· en· W6963097172 on OpenAlexaboutno aff

Bibliographic record

VenueCalifornia Digital Library · 2021
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsHoloceneTundraPeatSedimentDiatomEnvironmental changePollenPaleolimnologyPermafrostClimate change

Abstract

fetched live from OpenAlex

The purpose of this study was to understand how location within a landscape influences the response of aquatic and terrestrial ecosystems to climate change (with particular focus on how lake ecosystems respond to peatland development) at the tundra-forest border of northern Manitoba. In 2008-2009 we sampled water and surface sediment from 44 lakes. We collected sediment cores (3-4 meters in length) from 8 of these lakes. These cores represent 4000 - 8000 years of continuous sediment deposition. The data suggest a strong link between lake water chemistry and peatland in the modern landscape, a relatively stable mix of forest and tundra over the past 7500 years and a heterogeneous expansion of peatland beginning ~4500 years Before Present (BP). This dataset includes modern lake chemistry, diatom assemblages, and morphometry and land cover data for 44 lakes sampled primarily in 2009. Proxy data are also provided for sediment cores collected from 8 lakes. Data for the cores include Lead 210 and 14-Carbon dates, loss-on-ignition, grain size, carbon and nitrogen (concentrations and isotopes), extractable elements (Calcium, Magnesium, Iron, Potassium, Phosphorous), biogenic silica, X- Ray Fluorescence (XRF), magnetics (susceptibility, Anhysteretic Remanent Magnetization (ARM), Isothermal Remanent Magnetization (IRM)), charcoal, pollen and spores, and diatoms.

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 categoriesMeta-epidemiology (narrow), 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 score0.376
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.042

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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