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Record W2058290497 · doi:10.1029/2004jg000003

Long‐term trends in catchment export and lake retention of dissolved organic carbon, dissolved organic nitrogen, total iron, and total phosphorus: The Dorset, Ontario, study, 1978–1998

2005· article· en· W2058290497 on OpenAlexaffabout
Peter J. Dillon, Lewis A. Molot

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsYork UniversityTrent University
Fundersnot available
KeywordsDissolved organic carbonSurface runoffEnvironmental scienceTotal organic carbonHydrology (agriculture)Organic matterSurface waterNutrientPhosphorusDrainage basinEnvironmental chemistryEcologyChemistryGeologyEnvironmental engineeringGeographyBiology

Abstract

fetched live from OpenAlex

Annual catchment export of total phosphorus (TP), dissolved organic carbon (DOC), total iron (Fe), and dissolved organic nitrogen (DON) to seven lakes in central Ontario was measured between 1978 and 1998. Fluctuations in annual water discharge and total DOC load (including precipitation) to the lakes over the 20‐year period were similar in the seven study lakes. DOC export to the lakes responded proportionally to changes in discharge, decreasing during drier and warmer years. There were similar but less accentuated variations in annual DOC lake concentrations. There were no clear regional trends evident during the 20‐year period toward drier or wetter conditions, less DOC load, clearer lakes, etc., that could be interpreted as signaling a shift toward a different equilibrium state. The fraction of the DOC load retained by lakes (transferred to sediments and the atmosphere) increased during an extended dry period. Fe, TP, and DON export decreased more than DOC export during the extended dry period. Runoff appears to affect Fe, TP, and DON export first by controlling export of organic matter and second by affecting water table position and thus redox levels in the surface layer of peatlands. Permanently drier conditions with less runoff would likely lead to clearer lakes that are less productive. Conversely, increased runoff would lead to more colored and productive lakes. Both scenarios have implications for subsistence and sport fishing economies.

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 score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Citations140
Published2005
Admission routes2
Has abstractyes

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