Irish Drought Impacts Database v.1.0 (IDID)
Bibliographic record
Abstract
The Irish Drought Impacts Database (IDID) presents information from drought related newspaper articles for the island of Ireland, covering the period 1733 – 2019, collected through systematic searching of the Irish Newspaper Archive. These articles were identified, assessed and categorized using a modified version of the classification scheme employed by the European Drought Impact Inventory (EDII) (Stahl et al. 2012). The IDID provides information on the documented temporal and geographical extent of drought events, their socio-economic and political contexts, their consequences and mitigation strategies employed. Temporal information includes the newspaper publication date, timing and duration of drought periods and timing of impacts. Spatial details are provided on three different levels; in addition to Nomenclature of Territorial Units for Statistics Level 2 (Nuts2) regions, county location and, if available, a more localized place name (e.g., town or city name) is also recorded. The database allows the analysis of long-term patterns in drought incidence and impacts as well as offering insights into the impacts of individual drought events over nearly three centuries of Ireland’s history. Spatially specific data provide an opportunity for further exploration of the differential vulnerability of various geographical locations, which may vary depending on biophysical conditions, economic, political and societal context. Textual excerpts offer further insight into the human experience and perception of drought and its impacts. The IDID therefore enables a better understanding of drought, vulnerability, and offers a new open access tool for multi-disciplinary investigation of drought on the island of Ireland.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.059 |
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".