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Record W2019597333 · doi:10.1139/s03-035

Surface water chemistry of burned and undisturbed watersheds on the Boreal Plain: an ecoregion approach

2003· article· en· W2019597333 on OpenAlexvenueaboutno aff
Erik W. Allen, Ellie E. Prepas, Stephan Gabos, William M. J. Strachan, W Chen

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBorealEcoregionFoothillsTaigaEnvironmental scienceHydrology (agriculture)WatershedEcologyGeologyBiology

Abstract

fetched live from OpenAlex

The water chemistry of the euphotic zone in 12 lakes within burned and reference watersheds on Alberta's Boreal Plain was surveyed two years post-fire. Five burned and four reference lakes were located in the Boreal Foothills (mean elevation = 1048 m) and three reference lakes were situated at lower elevations in the Boreal Mixedwood ecoregion (748 m). Mean dissolved organic carbon (DOC) concentration in lake water from burned watersheds was 1.4-fold higher than in lake water from reference Foothills watersheds, whereas lake colour increased with the area of catchment burned divided by lake volume (r = 0.98). Reference Mixedwood lakes had higher mean total phosphorus (TP, 1.8-fold) and chlorophyll a (chl a; 4.4-fold) concentrations than reference Foothills lakes. Ten additional lakes from a previous study in boreal Alberta were used to further compare water chemistry between ecoregions. Boreal Mixedwood lakes (n = 13) had higher TP (2.3-fold), chl a (3-fold), and Ca 2+ + Mg 2+ (3.3-fold) concentrations than Boreal Foothills lakes (n = 9). Our data suggest an influence of forest fire on lake chemistry in the Boreal Foothills, and demonstrate the need for an ecoregion approach to detect the impacts of watershed disturbance on the Boreal Plain. Key words: watershed disturbance, forest fire, lake nutrients, lake elevation, phosphorus, chlorophyll a, anions, cations, dissolved organic carbon.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.282

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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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 designBench or experimental
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

Citations28
Published2003
Admission routes2
Has abstractyes

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