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Record W4240564119 · doi:10.1139/f00-108

Element export in runoff from eastern Canadian Boreal Shield drainage basins following forest harvesting and wildfires

2000· article· en· W4240564119 on OpenAlexfundvenueaboutno aff
Sébastien Lamontagne, Richard Carignan, Pierre D'Arcy, Yves T. Prairie, David Paré

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersGroupe de recherche interuniversitaire en limnologie
KeywordsBorealEnvironmental scienceSurface runoffDrainageHydrology (agriculture)TaigaNutrientDrainage basinBiogeochemical cycleLoggingGeologyEcologyForestryGeographyBiology

Abstract

fetched live from OpenAlex

Element export rates from the drainage area of nine harvested, nine burnt, and 16 reference Boreal Shield lake basins in Haute-Mauricie, Québec, were estimated for the 3 years following harvesting or fires. Export rates from the drainage area of the basins were evaluated using lake sampling surveys, estimated hydrological budgets, and estimated nutrient retention during lake transit. Increases in K + , total N, and total P export rates were similar between harvested and burnt drainage areas. However, harvested drainage areas exported more dissolved organic C, while burnt drainage areas exported more Mg 2+ , NO 3 - , and SO 4 2- . Potassium cumulative losses in runoff during the 3 years of the study were of a similar magnitude as volatilization losses during fires and corresponded to ~33% of the losses by biomass removal during harvesting. While the increased export rates for N and P following fires or harvesting represented negligible losses of nutrients for the forest, they were important supplementary inputs to lakes. The differences in element export rates observed between harvested and burnt drainage areas indicate that these disturbances have different impacts on biogeochemical cycles in the Boreal Shield forest.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.395

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 communication0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Citations102
Published2000
Admission routes3
Has abstractyes

Explore more

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