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<b>Multi-Walled Carbon Nanotube Application Alters Stomatal Behavior in Boreal Shrubs Under Drought Conditions</b>

2025· dataset· en· W7084067224 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typedataset
Languageen
FieldSocial Sciences
TopicComparative International Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBorealAbiotic componentTaigaSeedlingDrought stressAbiotic stressCarbon fibersEcosystem

Abstract

fetched live from OpenAlex

Seedling establishment on reclaimed boreal sites is often constrained by drought and other abiotic stresses. Recent studies have shown that the use of carbon nanomaterials can modify the stress physiology of crops, but their utility for native boreal species is largely unknown. We tested whether carboxylic acid–functionalized multi-walled carbon nanotubes (MWCNTs) alter drought responses in three shrubs common to western Canada: Canadian buffaloberry (Shepherdia canadensis (L.) Nutt.), red-osier dogwood (Cornus sericea L.), and low-bush cranberry (Viburnum edule).

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)0.004
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 score0.478
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0390.014

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 head0.052
GPT teacher head0.381
Teacher spread0.329 · 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

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