MétaCan
Menu
Back to cohort
Record W1990921510 · doi:10.1080/14634980590953770

Surface water quality in Manitoba with respect to six chemical parameters, water body and sediment type and land use

2005· article· en· W1990921510 on OpenAlexaffabout
Eva Pip

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsEnvironmental scienceOrganic matterTotal dissolved solidsWater qualityEffluentSurface waterHydrology (agriculture)SedimentNitrateDissolved organic carbonSurface runoffContaminationLand useEnvironmental chemistryEnvironmental engineeringEcologyChemistryGeology

Abstract

fetched live from OpenAlex

Surface waters at 425 sites in Manitoba were analyzed for total dissolved solids, nitrate-nitrogen, dissolved organic matter, cadmium, lead and copper. Regional differences in chemical parameters were found for various areas of the province and for some bottom sediment types. Sites were classified according to predominant type of land use: minimal, cropland, livestock, mining, recreation, forest logging, hydroelectric development and urban effluent. The highest mean values of total dissolved solids, cadmium and copper were associated with mining, although the highest absolute values for copper were found in cottage and recreational areas due to the use of copper sulphate to control algal blooms. Urban effluents and agriculture significantly elevated total dissolved solids and nitrate. Elevated dissolved organic matter was most associated with land clearing and urban effluents. The relative significance of the various human activities differed with geographical region. Streams were the most vulnerable to contamination, particularly on the Precambrian Shield. Water quality problems exist in some areas in the province, and management must take into account the regional characteristics of surface waters, as well as address the varying nature and intensity of contamination generated by human land uses.

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)0.002
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 score0.210
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.040
GPT teacher head0.296
Teacher spread0.256 · 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

Citations11
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueAquatic Ecosystem Health & ManagementSame topicWater Quality and Pollution AssessmentFrench-language works237,207