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Record W6913144373 · doi:10.5683/sp3/9q6nq4

Data for: Scale-dependent responses of understory vegetation to the physical structure of undisturbed tundra shrub patches

2022· dataset· en· W6913144373 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsTundraVegetation (pathology)ShrubUnderstoryAbiotic componentCanopySampling (signal processing)Plant community

Abstract

fetched live from OpenAlex

These data were collected in order to explore relationships between the physical structure of tundra shrubs and local vegetation and abiotic conditions. In order to do this we established 3 x 3 m sampling plots at the top, middle, and bottom of ten Alnus alnobetula (green alder) patches. All patches were situated on south-southeast facing hillslopes within 2 km of the Tail Valley Creek Research Station. Within each of these plots we selected two sub-plots for vegetation community composition. We also measured several abiotic variables within 50 cm of each 3 x 3 m plot. At each patch a 9 m belt was established from 13 m above the top edge to at least 12 m below the bottom edge, encompassing all 3 x 3 m sampling plots. Within this belt we mapped the relative location of all mature alder individuals and recorded their height and canopy width. These data were used to derive the structural data associated with each sampling plot and the data used for analysis of topographic trends in structural characteristics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.031

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 head0.050
GPT teacher head0.322
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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