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Record W12528823

ベーテ格子上のアンダーソンモデルにおける固有値・固有関数の分布について (繰りこみ群の数理科学での応用)

2008· article· en· W12528823 on OpenAlexaboutno aff
史彦 中野

Bibliographic record

Venue数理解析研究所講究録 · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGeology
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the results of an investigation into the effects of air emissions from sour gas processing plants on indices of retainment or survival of adult female dairy cattle on farms in Alberta; namely, the productive lifespan of individual animals, and annual herd-level risks for culling and mortality. Using a geographical information system, 2 dispersion models--1 simple and 1 complex--were used to assess historical exposures to sour gas emissions at 1382 dairy farm sites from 1985 through to 1994. Multivariable survival models, adjusting for the dependence of survival responses within a herd over time, as well as potential confounding variables, were utilized to determine associations between sour gas exposure estimates and the time from the first calving date to either death or culling of 150210 dairy cows. Generalized linear models were used to model the relationship between herd-level risks for culling and mortality and levels of sour gas exposure. No significant (P < 0.05) associations were found with the time to culling (n = 70052). However, both dispersion model exposure estimates were significantly associated (P < 0.05) with a decreased hazard for mortality; that is, in cases where cattle had died on-farm (n = 8743). There were no significant associations (P > 0.05) between herd culling risks and the 2 dispersion model exposure estimates. There was no measurable impact of plant emissions on the annual herd risk of mortality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.220
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
Published2008
Admission routes1
Has abstractyes

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