Ministry of Environment of Québec (2011-2014) web archive collection derivatives
Bibliographic record
Abstract
Web archive derivatives of the Ministry of Environment of Québec (2011-2014) collection from the Bibliothèque et Archives nationales du Québec. The derivatives were created with the Archives Unleashed Toolkit. Merci beaucoup banq! These derivatives are in the Apache Parquet format, which is a columnar storage format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See this notebook for examples. <strong>Domains</strong> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> Produces a DataFrame with the following columns: domain count <strong>Web Pages</strong> <pre><code class="language-java">.webpages()</code></pre> Produces a DataFrame with the following columns: crawl_date url mime_type_web_server mime_type_tika content <strong>Web Graph</strong> <pre><code class="language-java">.webgraph()</code></pre> Produces a DataFrame with the following columns: crawl_date src dest anchor <strong>Image Links</strong> <pre><code class="language-java">.imageLinks()</code></pre> Produces a DataFrame with the following columns: src image_url <strong>Binary Analysis</strong> Audio Images PDFs Presentation program files Spreadsheets Text files Videos Word processor files
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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".