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Record W2141389112 · doi:10.1098/rstb.2007.2197

Streamflow hydrology in the boreal region under the influences of climate and human interference

2007· article· en· W2141389112 on OpenAlexaffabout
Ming‐ko Woo, Robin Thorne, Kit K. Szeto, Daqing Yang

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change CanadaMcMaster University
Fundersnot available
KeywordsStreamflowEnvironmental scienceClimate changeBorealSubarctic climateClimatologyGlobal warmingDrainage basinHydrology (agriculture)EcologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

The boreal region has a subarctic climate that is subject to considerable inter-annual variability and is prone to impacts of future warming. Climate influences the seasonal streamflow regime which typically exhibits winter low flow, terminated by spring freshet, followed by summer flow recession. The effects of climatic variation on streamflow cannot be isolated with confidence but the impact of human regulation of rivers can greatly alter the natural flow rhythm, changing the timing of flow to suit human demands. The effect of scenario climate change on streamflow is explored through hydrological simulation. Example of a Canadian basin under warming scenario suggests that winter flow will increase, spring freshet dates will advance but peak flow will decline, as will summer flow due to enhanced evaporation. While this simulation was site specific, the results are qualitatively applicable to other boreal areas. Future studies should consider the role of human activities as their impacts on streamflow will be more profound than those due to climate change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.328
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.275
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations118
Published2007
Admission routes2
Has abstractyes

Explore more

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