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Record W3124950848

Raising Financing Through Strategic Timing

2017· article· en· W3124950848 on OpenAlexaff
Elicia Maine, V. J. Thomas

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTimelineVenture capitalBusinessRaising (metalworking)FinanceFund raisingValue (mathematics)Key (lock)New VenturesSocial venture capitalEntrepreneurshipEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Strategic timing can be key for nano-drug-delivery ventures to get financing. Timely publications engage potential partners; early broad, blocking, relevant patents demonstrate the potential to appropriate value; and venture formation closer to clinical viability better aligns its timeline with that of venture capitalists.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0200.020
Open science0.0020.012
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0630.018

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 head0.043
GPT teacher head0.312
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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