MétaCan
Menu
Back to cohort

Shrub ring width measurements of Alnus alnobetula and Salix spp. collected from the Inuvialuit Settlement region, Northwest Territories, Canada, 2022-2024 - VERSION 1.0

2025· dataset· en· W7124149670 on OpenAlexaboutno aff
Georgia M. Hole

Bibliographic record

VenueNERC Environmental Data Service · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsShrubSettlement (finance)DendrochronologyWoody plantWillow

Abstract

fetched live from OpenAlex

**** PLEASE BE ADVISED TO USE VERSION 2.0 DATA ***** The VERSION 2.0 data set (see 'Related Data Set Metadata' link below) has been extended with further samples. Shrub ring width datasets for Alnus alnobetula and Salix spp. Sample collection of alnus alnobetula and Salix spp. was undertaken in the Inuvialuit Settlement region, Northwest Territories, Canada during field campaigns in 2022, 2023, and 2024. Shrub stems were sampled from both living erect canopy-forming shrubs, and from beaver-browsed stems. This dataset consists of mean ring width (SRW) growth curves per individual shrub sampled. SRW values result from crossdating and averaging across the section and individual shrub level, following established shrub dendrochronological methods. The shrub ring width measurements are used for dendrochronological chronology formation and crossdating. Funding was provided by the UKRI CINUK programme grant NE/X002578/1.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.153
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.016

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 head0.022
GPT teacher head0.222
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; 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 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueNERC Environmental Data ServiceFrench-language works237,207