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Record W16323757 · doi:10.1021/es051382h

On Generalized Schur Numbers for x1+x2+c = kx3.

2007· article· en· W16323757 on OpenAlexvenueno aff
Bernie Martinelli, Daniel Schaal

Bibliographic record

VenueArs Combinatoria · 2007
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsSchur's theoremSchur complementCombinatoricsPure mathematicsOrthogonal polynomialsPhysicsEigenvalues and eigenvectors

Abstract

fetched live from OpenAlex

Asia is of global importance economically, yet data on ambient persistent organic pollutant levels are still sparse for the region, despite international efforts under the Stockholm Convention to identify and reduce emissions. A large-scale passive air sampling survey was therefore conducted in Asia, specifically in China, Japan, South Korea, and Singapore. Polyurethane foam disks were deployed simultaneously at 77 sites, between Sept 21 and Nov 16, 2004, and analyzed for polychlorinated biphenyls (PCBs), organochlorine compounds (hexachlorobenzene (HCB), dichlorodiphenyltrichloroethanes (DDTs), chlordane), and polybrominated diphenyl ethers (PBDEs). The meteorological conditions prevailing in the region at this time facilitated the assessment of local/regional differences in atmospheric emissions, because large-scale advection effects due to monsoons or dust storms did not occur. Air concentrations estimated assuming an average sampler uptake rate of 3.5 m3/day ranged as follows (pg m(-3)): PCBs, 5-340; HCB, 10-460; DDTs, 0.4-1800; chlordanes, 1-660; PBDEs, < 0.13-340. South Korea and Singapore generally had regionally low concentrations. Elevated concentrations of PCBs, DDTs, and HCB occurred at sites in China, higher than reported in a similar recent sampling campaign in Europe. Chlordane was highest in samples from Japan (which also had elevated levels of PCBs and DDTs) and was also elevated in some Chinese locations. PBDE levels were generally low in the region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations4
Published2007
Admission routes1
Has abstractyes

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