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Record W2891876297 · doi:10.1016/j.dib.2018.08.097

Computational, experimental details, and biological raw data accompanying the publication: “The synthesis and characterization of a nanomagnetite with potent antibacterial activity and low mammalian toxicity”

2018· article· en· W2891876297 on OpenAlex
S. Maryamdokht Taimoory, Abbas Rahdar, Mousa Aliahmad, Fardin Sadeghfar, Mohammad Reza Hajinezhad, Mohammad Jahantigh, P Shahbazi, John F. Trant

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Windsor
FundersUniversity of Sistan and BaluchestanUniversity of ZabolUniversity of Windsor
KeywordsReplicateAntibacterial activityRaw dataMagnetiteExperimental dataComputer scienceReplication (statistics)Research articleNanoparticleCombinatorial chemistryNanotechnologyChemistryComputational biologyData miningMaterials scienceBiologyBacteriaMetallurgyProgramming language

Abstract

fetched live from OpenAlex

This data file includes experimental details on how to make uncoated iron oxide nanoparticles using a green electrochemical method. It provides the raw data on the antibacterial activity of one of these formulations, and the full computational data and methodology used to generate that data, of several different magnetite clusters of specific spin multiplicities for 4, 5, 7 and 9 iron atom magnetite clusters. This data will assist other researchers wishing to replicate or expand on these results for the investigation and use of nanomagnetite for antibacterial applications.

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 categoriesnone
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 score0.574
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.049
GPT teacher head0.276
Teacher spread0.228 · 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