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Record W7110873478 · doi:10.12775/bgeo-2025-0014

Thermal regime of lakes in the Polish Lowlands in the light of climate change

2025· article· W7110873478 on OpenAlexaff

Bibliographic record

VenueBulletin of Geography Physical Geography Series · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpilimnionHypolimnionClimate changePeriod (music)Trend analysisWater level

Abstract

fetched live from OpenAlex

Based on data on surface water temperature, summer thermal structure and ice phenomena in lakes in northern Poland for the years 1961-2020, changes in their thermal regime during the period of climate change were determined. The average annual surface water temperatures in all lakes (13 lakes) were characterized by an average positive trend at the level of 0.044oC·year-1 with 0.015 oC·year-1 in January and 0.069 oC·year-1 in May. In turn, in the summer (from 20 July to 20 August), the average water temperature within the epilimnion (to a depth of 5 m) was characterized by a positive trend at the level of 0.05–0.07 oC·year-1, while below this layer a negative trend was noted in all lakes. A characteristic feature observed in all lakes was a decrease in the thickness of the epilimnion with a trend at the level of 0.07–0.11 m·year-1. The hypolimnion layer showed a negative trend for its water temperature, which ranged from 0.02oC·year-1 (Raduńskie Górne) to 0.07oC·year-1 (Miedwie). The ice cover appeared on average in the middle of the third decade of December and its trend was characterized by negative values ​​(0.1-0.2 days∙year-1). In turn, the dates of its disappearance were recorded earlier and earlier, most often in the second decade of March. The consequence of the initial and final dates with ice cover is the length of its occurrence, which was shortened on average by 0.7-0.8 days∙year-1. The general trend of shortening the period with ice cover was also accompanied by a clear decrease in its thickness (with a trend of (0.2-0.4 cm∙year-1).

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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.

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
Published2025
Admission routes1
Has abstractyes

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