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Record W1820764679 · doi:10.5194/bg-12-6999-2015

Impact of forest harvesting on water quality and fluorescence characteristics of dissolved organic matter in eastern Canadian Boreal Shield lakes in summer

2015· article· en· W1820764679 on OpenAlexafffundabout
Patricia Glaz, Jean‐Pierre Gagné, Philippe Archambault, Pascal Sirois, Christian Nozais

Bibliographic record

VenueBiogeosciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsDissolved organic carbonEnvironmental scienceBorealTaigaWater qualityHydrology (agriculture)Aquatic ecosystemOrganic matterEcosystemEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract. Forestry activities in the Canadian Boreal region have increased in the last decades, raising concerns about their potential impact on aquatic ecosystems. Water quality and fluorescence characteristics of dissolved organic matter (DOM) were measured over a 3-year period in eight eastern Boreal Shield lakes: four lakes were studied before, 1 and 2 years after forest harvesting (perturbed lakes) and compared with four undisturbed reference lakes (unperturbed lakes) sampled at the same time. ANOVAs showed a significant increase in total phosphorus (TP) in perturbed lakes when the three sampling dates were considered and in DOC concentrations when considering 1 year before and 1 year after the perturbation only. At 1 year post-clear cutting DOC concentrations were about 15 % greater in the perturbed lakes at ~ 15 mgC L−1 compared to 12.5 mgC L−1 in the unperturbed lakes. In contrast, absorbance and fluorescence measurements showed that all metrics remained within narrow ranges compared to the range observed in natural waters, indicating that forest harvesting did not affect the nature of DOM characterized with spectroscopic techniques. These results confirm an impact of forestry activities 1 year after the perturbation. However, this effect seems to be mitigated 2 years after, indicating that the system shows high resilience and may be able to return to its original condition in terms of water quality parameters assessed in this study.

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 score0.243
Threshold uncertainty score0.620

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 head0.034
GPT teacher head0.267
Teacher spread0.234 · 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

Citations16
Published2015
Admission routes3
Has abstractyes

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