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Record W4403650221 · doi:10.1002/agg2.20577

Short‐term survival and growth of 32 native boreal plants on treated oil sands tailings

2024· article· en· W4403650221 on OpenAlexafffund
Dani Degenhardt, Angeline Van Dongen, Çağdaş Kera Yücel, Amanda Schoonmaker

Bibliographic record

VenueAgrosystems Geosciences & Environment · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsNorthern Alberta Institute of TechnologyCanadian Forest Service
FundersCanadian Natural Resources Limited
KeywordsTailingsOil sandsBorealEnvironmental scienceTerm (time)EcologyGeographyBiologyArchaeologyAsphaltMetallurgyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The consolidation of oil sands tailings is a cost‐ and time‐consuming process that requires treatment via active (e.g., centrifugation) and passive (e.g., self‐weight consolidation) methods. The use of plants to dewater tailings is a promising concept and has previously been evaluated using agronomic grass species in greenhouse studies. This greenhouse study evaluated the short‐term survivorship and growth of 32 upland and lowland native plant species (12 forbs, 14 graminoids, and six woody plants) in centrifuged tailings and benchmarked their performance against reclamation soil and undisturbed forest soil. All plant species were propagated from seed and transplanted as seedlings into containers filled with one of the three substrates. After 42 days, the height (woody species only) and total aboveground biomass were determined for all living plants. As expected, the mortality of seedlings in tailings was higher than plants grown in the other two substrates. Graminoid species, regardless of species community type (wetland or upland), had higher survival probabilities and growth compared to forb or woody species across all substrates. Of forbs and woody species evaluated, Geum aleppicum and Populus tremuloides showed the most promise amongst the upland species, and Rumex occidentalis was the wetland equivalent.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes2
Has abstractyes

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