CRAWLING THE CONSTRUCTION WEB – A MACHINE-LEARNING APPROACH WITHOUT NEGATIVE EXAMPLES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
& Professionals and craftsmen in the construction sector make an intensive use of information in their decision-making processes but only make limited use of the abundant information that is potentially available to them, particularly on the web.Consequently, designs are impoverished, construction is defective, and innovation is delayed.To facilitate convivial access to focused information, we have developed a question-and-answer (Q-A) system (reported elsewhere).To support this system, we have developed an automated crawler that permits the establishment of a bank of relevant pages, adapted to the needs of this particular industry-user community.It is based on the machine-learning framework in which an intelligent decision unit is trained to distinguish between nontopic and informative pages.We show that standard approaches which use both positive and negative classes are sensitive to the noise in the negative class.We propose different techniques for learning without negative examples, since initially one only has limited, positive information labeled by human experts; they are evaluated.Our crawler that uses the positive examples-based learning (PEBL) framework is able to collect construction-oriented pages with high precision and discovery rate.It can also be used to build domain-specific collections of pages in different scientific or professional contexts.Currently, there is a pressing need to provide an answer to the following question: how can the engineer (or the architect) possess all the information that is properly required to make professionally correct decisions?Indeed, it has been shown that among the causes of loss of productivity in the building design and construction process, lack of effective access to information is the single most significant factor (Mohsini and Davidson 1991).
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it