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Record W7132929359

The effect of marine acidification on marine organisms

2020· dissertation· hr· W7132929359 on OpenAlexaboutno aff
Ivan Škegro

Bibliographic record

VenueRepository of the Faculty of Science, University of Zagreb · 2020
Typedissertation
Languagehr
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaMercury (programming language)Black seaHeavy metals
DOInot available

Abstract

fetched live from OpenAlex

Oceanski unos antropogenog ugljičnog dioksida (CO2) mijenja kemiju morskih voda u svjetskim oceanima s posljedicama na morski život. Povišeni parcijalni tlak CO2 (pCO2) uzrokuje gomilanje zasićenja kalcijevim karbonatom u mnogim regijama, osobito u visokim geogragfskim širinama i regijama koje se presijecaju s izraženim hipoksičkim zonama. Sposobnost morskih životinja, što je najvažnije kod pteropodnih mekušaca, foraminifera i nekih bentoskih beskralješnjaka, da bi stvorili skeletne strukture od CaCO3 izravno su pod utjecajem kemije CO2 morskih voda. CO2 utječe i na fiziologiju morskih organizama, zbog kiselo-bazne (ne)ravnoteže i smanjenog transportnog kapaciteta kisika. Nekoliko studija trenutačnih razina pCO2 ometaju buduću sposobnost predviđanja utjecaja na dinamiku hranidbene mreže i ostalih procesa ekosustava. Ovdje su predstavljena nova zapažanja, pregled dostupnih podataka i identificikacija prioriteta za buduća istraživanja, zasnovanih na regijama, ekosustavima, svojtama i fiziološkim procesima za koje se vjeruje da su najosjetljiviji na zakiseljavanje oceana. Acidifikacija oceana i sinergistički utjecaji drugih antropogenih utjecaja pružaju veliki potencijal za širenje (negativnih) promjena u morskim ekosustavima.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.007
GPT teacher head0.205
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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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