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Record W34613702 · doi:10.1021/acs.est.1c04111

Rigorous analysis of heuristics for NP-hard problems.

2005· article· en· W34613702 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSymposium on Discrete Algorithms · 2005
Typearticle
Languageen
FieldComputer Science
TopicComplexity and Algorithms in Graphs
Canadian institutionsnot available
FundersCanada First Research Excellence Fund
KeywordsHeuristicsHeuristicComputer scienceMathematical optimizationContext (archaeology)AlgorithmTheoretical computer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

With no single carbon capture and sequestration solution able to limit the global temperature rise to 1.5-2.0 °C by 2100, additional climate stabilization measures are needed to complement current mitigation approaches. Urban farming presents an easy-to-adopt pathway toward carbon neutrality, unlocking extensive urban surface areas that can be leveraged to grow food while sequestering CO<sub>2</sub>. Urban farming involves extensive surface areas, such as roofs, balconies, and vertical spaces, allowing for soil presence and atmospheric carbon sequestration through air-to-soil contact. In this viewpoint we also advocate the incorporation of enhanced rock weathering (ERW) into urban farming, providing a further opportunity for this recognized negative emissions technology that is gaining momentum worldwide to gain greater utilization.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.800
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
Bibliometrics0.0010.002
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 head0.024
GPT teacher head0.273
Teacher spread0.250 · 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