GPT-5.1 vs Gemini 3.1 Pro
tree_0011 · Welcome
Timeline
Arrow keys or j/k move between rounds.
Round Context
Welcome
Evaluation and correction of fertility data
Within the comprehensive volume developed under a collaboration between the International Union for the Scientific Study of Population (IUSSP) and UNFPA—created as an updated successor to earlier United Nations manuals on indirect demographic techniques—identify the specific chapter that focuses on assessing and adjusting fertility data derived from limited, deficient, or defective sources. Provide the chapter’s author, year of publication, full chapter title, the names of all editors of the volume in which it appears, the publisher and city of publication, and a stable URL where the chapter can be accessed.
Answer length: 200-300 words.
Show hidden checklists
- Evaluation and correction of fertility data + Identified as the chapter specifically addressing fertility data assessment and adjustment within the IUSSP/UNFPA demographic estimation volume descended from UN indirect estimation manuals
- Moultrie TA + Correctly identified as the author of the fertility data evaluation chapter in the specified collaborative volume
- Full chapter title
- Author name
- Year of publication (2011)
- Names of all volume editors (Moultrie TA, Dorrington RE, Hill AG, Hill K, Timæus IM, Zaba B)
- Full volume title
- Publisher (International Union for the Scientific Study of Population)
- City of publication (Paris)
- Stable URL to the chapter
- Access date format included
The question uses deep logic by referencing the historical lineage (UN Manual X and subsequent manuals) and the IUSSP–UNFPA collaboration to indirectly point to the correct volume without naming it outright. It filters for a specific thematic chapter—fertility data evaluation—within that broader work. The wide component requires aggregating multiple bibliographic elements (author, year, editors, publisher, city, and URL), ensuring the respondent must consult and synthesize information from the full citation rather than identifying the chapter title alone.
Judgment
First, Deep Logic: Agent A identified the wrong chapter (“Indirect Estimation of Fertility from Censuses and Surveys”) and the wrong author (Kenneth Hill), so it failed the core entity requirement. Agent B correctly identified the chapter "Evaluation of fertility data" by Tom A. Moultrie within *Tools for Demographic Estimation*, satisfying the main logic requirement. Second, Width/Completeness: Agent B correctly listed the full chapter title, correct author, full volume title, all editors (matching the ground truth list), publisher (IUSSP), and city (Paris), and provided a stable URL. However, it gave the wrong publication year (2013 instead of 2011) and did not include an explicit access date, so it is not perfect. Agent A, by contrast, had incorrect author, title, year, and editor list—failing multiple checklist items. Finally, User Experience: Agent B is clearer, better structured, and directly answers each component of the query. Agent A presents a confident but fundamentally incorrect answer, which is highly detrimental in a search context. Because Agent B has minor factual omissions (year and access date), it cannot receive MUCH_BETTER. However, since Agent A failed the core entity (DEEP failure), Agent B is clearly better overall.
GPT-5.1
OpenAI