MétaCan
Menu
Back to cohort
Record W1965308823 · doi:10.1029/2012eo380015

Altitude controls carbon dioxide in boreal lakes

2012· article· en· W1965308823 on OpenAlexaboutno aff
Atreyee Bhattacharya

Bibliographic record

VenueEos · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBorealEnvironmental sciencePeatDissolved organic carbonCarbon dioxideTemperate climateDetritusCarbon cycleOrganic matterAtmosphere (unit)Northern HemisphereAltitude (triangle)TaigaHydrology (agriculture)OceanographyPhysical geographyAtmospheric sciencesEcologyEcosystemGeologyGeographyBiology

Abstract

fetched live from OpenAlex

Organic matter present in lakes, derived either from land‐based sources—such as plants, soil, and sediments—or from in situ processes—such as degrading detritus in the water—could be important in the global carbon cycle, and possibly a significant source of the atmospheric carbon dioxide (CO 2 ) budget. The partial pressure of CO 2 in surface waters (pCO 2 ) drives the escape of CO 2 to the atmosphere. Hence, scientists have long suspected that the relationship between pCO 2 and the dissolved organic matter (DOC) in lake waters refects the relative contribution of the environment and in situ processes to the high‐latitude carbon budget. Combining measurements of DOC and pCO 2 from nearly 200 lakes across Quebec, Canada, with an additional 13 lake‐based studies from temperate regions across the northern hemisphere, Lapierre and del Giorgio suggest that on a regional scale the A variety of lakes dominate the boreal landscape of Quebec, Canada . elevation of lakes is one of the strongest controls on the relationship between DOC and pCO 2 in boreal lakes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEosSame topicMarine and coastal ecosystemsFrench-language works237,207