Bibliographic record
Abstract
In modern society, a huge volume of data (big data) are being collected and accumulated, and growing exponentially.1 2 Data come from the internet, social media, medical records, customer databases, massive open data and other data sources. Big data are huge in quantity (volume), fast in production sometimes in real time (velocity), structured and unstructured in data types (variety), inconsistent and changing (variability), uncertain in data quality (veracity) and potentially useful (value).3 4 Big data, when analysed properly, can improve corporate performance, increase productivity, provide insights and predict future scenarios for better decisions in medicine, public health, science and technology, business and other sectors.5 6 Big data, when misused, can lead to data breach and privacy concerns6 and, when misinterpreted7 or manipulated,8 can cause false predictions leading to potentially disastrous consequences. Here is a small story that might help understand the impacts of big data. It is in part inspired by some ideas already posted on the internet.9 10 The story is about a customer who orders pizza by phone, and the interesting exchange that ensues, delightful and not so delightful, that potentially can happen during the big data era. > Pizza: [The phone rings] Thanks for calling. How may I help you? > Customer: I would like to order a pizza. > Pizza: What is your customer number? > Customer: XY1357 > Pizza: Hi, Mr. Lee. Your address …
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 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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.012 |
| 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 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".