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Record W4416860727 · doi:10.1680/jenge.24.00162

Transpiration measurements on vegetated soil layers over waste rock and tailings

2025· article· en· W4416860727 on OpenAlexaffabout
Léo Lamarche, Marie Guittonny, Bruno Bussière

Bibliographic record

VenueEnvironmental Geotechnics · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsTranspirationTailingsLand reclamationContext (archaeology)TopsoilWater balanceVegetation (pathology)GroundwaterHydrology (agriculture)

Abstract

fetched live from OpenAlex

Numerous large-scale hard rock mines operate in Canada and will need to be reclaimed. The water balance of the reclaimed sites needs to be measured regularly to assess the reclamation performance and prevent environmental contamination from mining wastes. Transpiration is an important component of the water balance. However, it is rarely directly measured and is typically deduced from the measurement of other water balance components. This study used non-destructive sap flow sensors to evaluate transpiration rates of willow cuttings (Salix miyabeana clone sx64) planted in 2017 on experimental cells (ECs) composed of tailings and waste rock covered with overburden and topsoil layers. Data were collected in late summer 2020 and 2021. Consistently low transpiration rates of 0.76 mm and 0.8 mm per day were measured for waste rock ECs in 2020 and 2021, and 0.63 mm per day for tailings ECs in 2021. Transpiration accounted for almost one-third of the cumulative precipitation on the tailings and waste rock ECs (varying from 16% to 32%). Such high values demonstrate the importance of measuring the transpiration component of the water balance and its evolution in relation to vegetation development in the mining reclamation context to improve post-closure water management.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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