You are a senior software architect assisting with the design of a scalable backend (Go + PostgreSQL) for a Flutter application called "Super Logger".

**Context:**
Super Logger tracks various data types: primitives (integer, double, string, color, image, etc.) and complex 'composite' types (JSON-like nested structures). Users create "Loggables" (definitions of things to track) and add "Logs" (timestamped values) to them. The app needs a backend to support thousands of users, each potentially creating thousands of logs daily across many loggables.

**Core Problem:** Efficiently storing and querying diverse log entry types at scale in PostgreSQL. The initial mobile app used a single table with a JSON blob for the log value, which doesn't scale well for backend querying.

**Two Main Backend DB Designs Explored:**

1.  **Hybrid Model (Model H):**
    *   A central `logs` table stores common metadata (`id`, `loggable_id`, `user_id`, `timestamp`, `note`) and a `log_type` discriminator column (enum or varchar).
    *   Type-specific extension tables (`integer_logs`, `text_logs`, `json_logs`, etc.) store the actual `value`, linked by `log_id` (FK to `logs.id`).

2.  **Separate Tables Model (Model S):**
    *   No central `logs` table.
    *   Separate tables for each log type (`integer_logs`, `text_logs`, etc.). Each table contains *all* common fields (`id`, `loggable_id`, `user_id`, `timestamp`, `note`) plus the type-specific `value`.

**Key Query Patterns Analyzed:**

1.  **Query 1:** Get all logs for a *specific loggable* within a date range.
    *   *Analysis:* Both models perform well. Model S might be slightly simpler if the type is known beforehand.
2.  **Query 2:** Get all logs for a *specific user* within a date range (User Timeline View).
    *   *Analysis (Critical):*
        *   Model H uses `LEFT JOIN`s from `logs` to extension tables. Bottleneck: Number of joins, but filtering happens early on the indexed `logs` table. Easier partitioning management.
        *   Model S uses `UNION ALL` across all type-specific tables. Bottleneck: Sorting the potentially massive combined result set *before* applying `LIMIT`. Requires partitioning many tables.

**Current Status:**
To empirically compare performance, we have:
1.  Created PostgreSQL schemas for both `model_h` and `model_s` with 4 log types (integer, double, text, json).
2.  Written and executed a Go script to populate both schemas with data: 5 users, each having one loggable per type, with 50,000 logs per loggable type (1 Million total logs).
3.  Defined the SQL queries (including `EXPLAIN ANALYZE`) for the two key patterns (Query 1 and Query 2) to be run against both schemas.

**Your Task:**
Analyze the likely results of running the benchmark queries. Discuss the expected performance differences between Model H and Model S based on the `EXPLAIN ANALYZE` output for Query 1 and Query 2. Consider factors like index usage, join costs, sort costs, sequential vs. index scans, and overall execution time. Recommend which model appears more suitable for the Super Logger backend based on this anticipated benchmark outcome, particularly focusing on the scalability of the User Timeline query (Query 2). Discuss potential optimizations like partitioning in more detail for the recommended model.