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Record W6967010226 · doi:10.5061/dryad.crjdfn3b7

Ground-dwelling invertebrates and plants following the application of inverted soil mounding on seismic lines

2023· dataset· en· W6967010226 on OpenAlexaffabout

Bibliographic record

VenueOpen MIND · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsBorealPeatInvertebrateTaigaNatural (archaeology)UnderstoryWildlife

Abstract

fetched live from OpenAlex

In northern Alberta, Canada, much of treed boreal peatlands are fragmented by seismic lines – linear disturbances where trees and shrubs are cleared for the exploration of fossil fuel reserves. Seismic lines have been shown to have slow tree regeneration, likely due to the loss of microtopography during the creation of seismic lines. Inverted soil mounding is one of the treatments commonly applied in Alberta to restore seismic lines and to mitigate the use of these corridors by wildlife and humans. In 2018, we assessed the effects of mounding on understory plants and arthropod assemblages, three years after treatment application. We sampled in five mounded and five untreated seismic lines, and in their adjacent treed fens (reference fens) within the Canadian Natural Resources Ltd (CNRL) Kirby South in-situ steam-assisted gravity drainage (SAGD) Plant, in the Athabasca oil sands (55°22'37.2" N, 111°10'3" W) of NW Alberta. Here we provide the species composition at these sites.

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)0.001
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 score0.967
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.058
GPT teacher head0.326
Teacher spread0.268 · 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
Published2023
Admission routes2
Has abstractyes

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