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Record W2061425787 · doi:10.1139/f06-007

Base cation reservoirs in soil control the buffering capacity of lakes in forested catchments

2006· article· en· W2061425787 on OpenAlexvenueno aff
Daniel Houle, Rock Ouimet, Suzanne Couture, Christian Gagnon

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAcid neutralizing capacitySoil waterEnvironmental scienceWatershedHydrology (agriculture)Drainage basinSurface waterSoil acidificationSTREAMSBase (topology)Soil pHSoil scienceEnvironmental chemistryGeologyChemistryAcid depositionEnvironmental engineeringGeography

Abstract

fetched live from OpenAlex

The acidification of forest soils and surface waters and their relatively poor recovery record following reductions in atmospheric sulphur emissions is a major ongoing environmental problem, particularly in northeastern North America. The slow recovery of surface water is widely hypothesized to result from depletion of reservoirs of base cations in soil. This is concordant with the theory that the acid-neutralizing capacity (ANC) of lakes is likely proportional to the size of the exchangeable base cation reservoirs present in surrounding watershed soils. However, data describing these linkages are still nonexistent in the literature. Here we show that lake ANC is highly predictable (r 2 = 0.75) based on the size of the exchangeable Ca 2+ reservoir in soil in 21 catchments representative of soil and lake conditions encountered in northeastern North America. This finding indirectly supports the hypothesis that the poor recovery of surface water from acidification is governed by the size of base cation reservoirs present in catchment soils. The size of the base cation reservoir in soil is thus a strong indicator of the acid–base status of both soils and surface waters.

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 score0.376
Threshold uncertainty score0.821

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 studies0.0000.001
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 head0.013
GPT teacher head0.190
Teacher spread0.177 · 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

Citations62
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicSoil and Water Nutrient DynamicsFrench-language works237,207