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Record W7036601255

A critical examination of chemical extremes in freshwater systems

2015· dissertation· en· W7036601255 on OpenAlexaff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsLakehead University
Fundersnot available
KeywordsEutrophicationAcid neutralizing capacityWater qualityHydrology (agriculture)Surface runoffWatershedClimate changeSampling (signal processing)
DOInot available

Abstract

fetched live from OpenAlex

The objectives of this thesis are to explore and identify: 1) the causative factors for
\nextreme endpoints in freshwater chemistry, specifically eutrophication and
\nacidification, and 2) the convergence of anthropogenic pollution, watershed
\ncomposition and climate effects that could contribute to the occurrence of freshwater
\nchemical extremes. Eutrophication is reviewed as a well-studied water quality issue
\nthat remains relevant as a management challenge. Extra focus is given to acidification,
\nquantified as a decrease in acid neutralizing capacity (ANC), because it has a strong
\ninfluence on physical properties such as nutrient (i.e., phosphorus, nitrogen)
\nresuspension that can potentially leading to chemical extremes.
\nData from lakes in the western Great Lakes region are examined with respect to effects
\nof acid inputs on in-lake ANC and pH response. Although drainage systems are
\ndiscussed, special attention is paid to softwater seepage lakes as being the most
\nsensitive with regards to acidification risk. The challenges of using data-intensive mass
\nbalance models in lakes with intermittent sampling histories lead to development of a
\nsimpler model for estimating open-water ANC in data-sparse locations. Acid input
\nsources are compared as combinations of area-weighted charge balances using publicly
\navailable data from long term monitoring programs. Weighted data combinations are
\nthen analyzed using maximum likelihood methods suitable for use with observational
\ndata. The final model correctly predicted low ANC events (ANC < 25 ?eq L-1 ) 20 out of
\n24 times (R2 = 0.50 adjusted for small sample size; 168 observations), but
\nunderestimates the severity of the lowest extremes.
\nThree factors stand out as being strongly related to acidification risk during the open
\nwater season: 1) volume of snowmelt, 2) in-lake ANC following spring turnover, and 3) pulsed runoff from associated wetland soils following drought and re-wetting events.
\nRecommendations for future research focus on quantifying acid and nutrient content
\nin pulsed runoff events and their impacts on freshwater systems given antecedent
\nconditions in both lakes and connected wetlands.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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