Bibliographic record
Abstract
In this paper, we reviewed the development background and the concept of the auction online, and then introduced the relation research works on this area. On this foundation, this text put forward a economics model of Auction online with mathematic description, which include three parts, valuation from the auction attendees, expected payment and probability of win, This is a Economics model of the Auction online Based E-commerce. Keywords: Auction, Auction online, E-commerce Resume Cet article evoque d’abord le contexte de developpement et la notion de l’enchere sur Internet, puis presente la situation de la recherche de ce domaine. Ensuite il propose le modele de science economique de l’enchere sur Internet et effectue une description numerique sous les trois angles : evaluation, paiement prealable et succes de l’encherisseur. En fin de compte, l’auteur etablit le modele de science economique de l’enchere sur Internet du commerce electronique. Mots-cles : enchere, enchere sur Internet, commerce electronique 摘要 本文首先回顧了網路拍賣的發展背景和概念意義，然後介紹了相關領域的研究進展情況，在此基礎上，本文提出了網路拍賣的經濟學模型，從競買人估價、預期支付和贏得拍賣品的概率這三個角度對網路拍賣進行了數學描述，建立了電子商務的網路拍賣經濟學模型。 關鍵詞：拍賣；網路拍賣；電子商務
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".