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Record W7094285059

Thomas Mcindoe: a Te Aroha saddler who became an Auckland businessman

2016· report· en· W7094285059 on OpenAlexaff

Bibliographic record

VenueResearch Commons (University of Waikato) · 2016
Typereport
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsPrince Albert Grand Council
Fundersnot available
KeywordsNucleofectionTSG101HyporeflexiaGestational periodLiquationPretextDiafiltrationArticular cartilage damage
DOInot available

Abstract

fetched live from OpenAlex

Starting out as a saddler at Te Aroha in 1891, Thomas McIndoe also became an agent, especially a successful land agent, acquiring some land holdings for himself. After leaving Te Aroha in 1911 he was a businessman in Auckland for the rest of his life. During the mining boom of the 1890s, he invested in many local mines, probably without making much if any money from his share dealings. McIndoe participated in almost every aspect of Te Aroha life, including the Anglican Church, a variety of sports, the Volunteers, the freemasons, and (especially) musical events. Involved in just about every local organization and local government body, he was the first president of the Chamber of Commerce and, briefly, on the borough council. Politically, he was a prominent supporter of the Liberal Party. In addition, he was notable for his charitable acts and for one heroic rescue. His personality was generally amiable, but he had a prickly side as well. He was a notable example of a ‘pillar of the local community’.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.147
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0230.003

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 head0.154
GPT teacher head0.378
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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