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Record W7006214089

Toronto is one of the worst places to buy real estate: report

2022· other· en· W7006214089 on OpenAlexaboutno aff

Bibliographic record

VenueInternet Archive (Internet Archive) · 2022
Typeother
Languageen
FieldChemistry
TopicChemistry and Stereochemistry Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateResidential real estatePlacemakingEscarpmentMarket researchEstate
DOInot available

Abstract

fetched live from OpenAlex

Rob Golfi, Sales Representative with RE/MAX Escarpment Realty, The Golfi Team, is joined by Madelyn Townes from Revel Realty Inc. in this edition of the Golfi Real Estate Show, Hamilton Edition.They discuss the May real estate statistics in Hamilton, Burlington and Niagara, as well as changes to the first-time homebuyer's program.Will Doug Ford's promise for the housing market be good news for home seekers?A new report shows Toronto is one of the worst places to buy real estate.And many Ontario renters are being forced to choose between food and paying rent.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.152
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1520.000

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 head0.245
Teacher spread0.232 · 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.

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
Published2022
Admission routes1
Has abstractyes

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