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Causes and consequences of changing oxygen availability in lakes

2023· preprint· en· W4319313367 on OpenAlexfundno aff
Cayelan C. Carey

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
FundersGlobal Lake Ecological Observatory NetworkNational Science Foundation
KeywordsEcosystemEnvironmental scienceWater qualityEcosystem servicesOxygenSurface waterClimate changeHydrology (agriculture)EcologyEnvironmental engineeringGeologyBiologyChemistry

Abstract

fetched live from OpenAlex

Changing oxygen availability in lakes and reservoirs is a fundamental limnological challenge of our time, with massive consequences for freshwater ecosystem functioning and water quality. Cross-lake surveys, paleolimnological studies, and long-term monitoring records indicate that many lakes are exhibiting declines in both surface and bottom-water oxygen availability due to climate and land use change, though a few lakes are exhibiting increases in oxygen. By analyzing time series of oxygen monitoring data from ~400 lakes, I find that some lakes may be experiencing a decoupling of surface and bottom oxygen dynamics: variability in surface oxygen concentrations is decreasing in some lakes while variability in bottom oxygen concentrations is increasing. Changes in both oxygen concentrations and variability have many implications for lake functioning because oxygen concentrations control many ecosystem processes. Consequently, lake ecosystem provisioning and cultural services (e.g., drinking water, fisheries, recreation) will likely be impaired by declining oxygen, whereas the effects of changing oxygen on regulatory and supporting ecosystem services (e.g., nitrate removal through denitrification,

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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