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Record W4390500493 · doi:10.12775/bgeo-2023-0008

Thermal regimes of lakes in the southern and eastern part of the Baltic Sea catchment area during the period of climate change

2023· article· en· W4390500493 on OpenAlexaff
Rajmund Skowron, Tomasz Jaszczyk

Bibliographic record

VenueBulletin of Geography Physical Geography Series · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIntegrated Water Resources Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceClimate changePeriod (music)Physical geographyClimatologyBaltic seaDrainage basinHydrology (agriculture)Surface waterGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

The study covered eleven lakes located in northern Poland and Belarus, aiming to determine the degree and trends of surface water temperature changes for the years 1971–2020 and lake ice cover for the period of 1961–2020 under the influence of ongoing climate changes. The analysis focused on changes and trends in average monthly surface water temperatures (SWT), as well as changes in the onset and end, duration and maximum thickness of ice cover. The average annual surface water temperature increased in the lakes by an average of 0.044°C per year. The lake with the highest trend is Chervonoe, at with 0.066°C per year, while the lowest is 0.029°C per year in Lake Hańcza. The ice cover duration showed a decrease of 0.6 days per year, and there was a reduction in maximum ice thickness of 0.27 cm per year

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)0.000
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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