(EP: 158) Helpful Tips For Home Buyers - Spring 2022
Bibliographic record
Abstract
It's springtime, and Winnipeg home buyers are facing a very tough sellers market. How can first time buyers get a leg up in this highly competitive environment? Lets talk with mortgage expert, my friend Mike Schroeder of Mortgage Architects...next. Home buyers in Winnipeg are in a very tuff spot. Our spring market is normally very competitive. This is when buyers come out in large numbers, and end up competing on homes and even condos.This year they are facing 2 additional obstacles,: Lack of listings and rising interest rates.Normally at this time , we have around 1200 houses on the market. This morning, we have just over 400. Whats worse, is that over half of them are new listings, and will likely be sold in the next couple of days.Also, the headlines are shouting \\"Bank of Canada DOUBLES its interest rates\\"... adding more stress to the daily routines of home buyers.So I thought it was high time that we speak with someone who knows mortgages, knows how to help home buyers and can add some perspective to these issues.Let's say hellow to Mike Schroeder of Mortgage Architects in Winnipeg.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.689 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".