Bibliographic record
Abstract
Historical extreme events such as floods and droughts can be documented in a variety of ways. These include meteorological observations and social reports, with narrative descriptions of extreme events and accompanying impacts. Here we look at how both these means of recording events can complement or contradict each other, in a quest to examine extreme events from quantitative observations and disruptive events from a descriptive viewpoint. The NORTHERN (Nineteenth-century Overseas Records Transcribed for Historical Environmental Reconstruction of the North) database contains over 1.8 million observations from 45 stations and 15 categories of weather observation type. Included in these are both numerical observations of precipitation events, including amounts, timing, and duration of events. Floods are the weather event most often described in newspapers and are occasionally captured in these precipitation fields. Flooding is often the outcome of complex seasons-long interactions between groundwater recharge, spring thawing of the ground, and snowmelt. Drought and the often-corresponding wildfires are similarly of great concern in many parts of Canada and tend to be recorded more in descriptive fields such as weather remarks and casual phenomena. By comparing the numerical and descriptive sources in the past and present, we can gain a better understanding of the nature of these events and the impacts, vulnerabilities and resiliencies to these events.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".