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Record W7132181915

Water dynamics in Europe due to climate change

2018· dissertation· hr· W7132181915 on OpenAlexaboutno aff
Sara Šariri

Bibliographic record

VenueRepository of the Faculty of Science, University of Zagreb · 2018
Typedissertation
Languagehr
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness managementQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Prirodni su protočni režimi rijeka u Europi već bitno izmijenjeni djelovanjem čovjeka: izgradnjom brana, nasipa i kanala, ali također i crpljenjem vode za potrebe stanovništva. Klimatske promjene predstavljaju dodatan rizik. Glavni elementi klime, čije će promjene utjecati na hidrološki ciklus i protočne režime rijeka, su oborine, temperatura i snježni pokrov. Same promjene tih elemenata i njihove posljedice bit će drugačije u pojedinim klimatskim zonama Europe. Najveći stupanj promjene može se očekivati u mediteranskoj i borealnoj klimatskoj zoni, a najmanji u oceanskom dijelu umjerene. Doći će do promjene obujma i vremena javljanja ekstremnih protoka, što će biti bitno za učestalost javljanja i intenzitet riječnih poplava. Očekuje se da će se one u budućnosti povećati kao rezultat daljnjeg ekonomskog rasta i klimatskih promjena. Prosječno vrijeme poplavljivanja u toku godine u Europi se postupno mijenja kako od zapada prema istoku, zbog povećanja udaljenosti od Atlantskog oceana, tako i od juga prema sjeveru, zbog povećanja utjecaja procesa povezanih sa snijegom. U seminarskom je radu za svaku od šest europskih klimatskih zona objašnjeno kako bi tipični protočni režimi mogli izgledati 2050ih pod utjecajem isključivo klimatskih promjena i prikazano hidrogramom reprezentativne rijeke. Klimatske će promjene imati velike posljedice, kako na ekosustave kopnenih voda, tako i na raspoloživost slatke vode za ljudsku upotrebu i neophodno je pažljivo upravljanje vodnim resursima kako bi se te posljedice ublažile.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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
Published2018
Admission routes1
Has abstractyes

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