Bibliographic record
Abstract
In 1997, 100,000 cases exceed the total number of auction case after IMF. And 10 trillion exceed the scale of a market of the real estate auction. In addition, In 2011, 1 quarter, the non-performing loans ratio of the bank is the tendency that 1 trillion of previous quarter contrast increases. the real estate auction is various. There are lots of the variable elements and the peoples undergo many crosses in the auction participation. Therefore, in this research, the conform tries to be presented so that the winning bid decision factor of the real estate auction is investigated and the bidder is able to make the reasonable decision-making which is not subjective decision-making in the complicated real estate auction market. In this research, the winning bid crystal started the assumption that it referred to the independent variable of the preceding researches and it is affected by the internal of the auction and external factor the research with the premise.The time horizon of the research was 11 months in 2010. The spatial range built data with the goods of Seoul eastern district court. By using the logistic regression analysis as the method of study, it analyze empitically. The more the result lowest price of the analysis was low, the more the contract price was high, the more the lowest price to contract price was high, the more the earth scale was small, it was exposed to be influenced by the winning bid crystal as the auction application subject was the person.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".