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Record W2041387722 · doi:10.1139/x08-071

Wildfire impacts on nitrogen concentration and production from headwater streams in southern Alberta’s Rocky Mountains

2008· article· en· W2041387722 on OpenAlexafffundvenueabout
Kevin D. Bladon, U. Silins, Michael J. Wagner, Micheal Stone, Monica B. Emelko, C. A. Mendoza, K. J. Devito, Sarah Boon

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowmeltSTREAMSEnvironmental scienceHydrology (agriculture)WatershedNitrogenNitratePrecipitationSurface runoffEcologyChemistryGeographyGeologyBiology

Abstract

fetched live from OpenAlex

The objective of this study was to examine initial effects of the 2003 Lost Creek wildfire (southwestern Rocky Mountains of Alberta) on concentrations and production (yield and total export) of several nitrogen (N) forms, and to explore initial recovery of these effects within the first 3 years after the fire. During the first postfire year, nitrate (NO 3 – ), dissolved organic nitrogen (DON), and total nitrogen (TN) concentrations in severely burned watershed streams were 6.5, 4.1, and 5.3 times greater, respectively, than those in reference streams. Weaker effects were evident for concentrations of ammonium (NH 4 + ; 1.5 times) and total particulate nitrogen (TPN; 3.0 times). A rapid decline in mean watershed concentrations and production of NO 3 – , DON, total dissolved nitrogen (TDN), and TN was observed from burned watersheds over the three seasons after the fire. However, elevated NO 3 – , TDN, and TN concentrations and production were still evident during the snowmelt freshet and following precipitation events after 3 years. Effects of the burn were strongly influenced by the regional flow regime, with the most elevated N concentrations and production occurring during higher discharge periods (snowmelt freshet and storm flows).

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 score0.282
Threshold uncertainty score0.375

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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations115
Published2008
Admission routes4
Has abstractyes

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