feat!: name the unknown key, expose tail probabilities, flag short fits

Three issues from two downstream consumers, all small, all sharing a
theme: the crate had the information and would not hand it over.

#44 — `UnknownKey { team: 0, member: 0 }` did not say which key. A
consumer upgrading 0.1.2 -> 0.4.1 had every one of 5591 predictions
return this error, fell back to a neutral 0.5, and lost its entire
metadata model for a day. Nothing crashed and nothing logged; it was
found by sweeping an unrelated parameter and noticing the output did not
move. The 0.4.0 change that made unknown keys an error was right — the
error was just too anonymous to act on. It now carries the key's `Debug`
rendering, and its `Display` says what to do about it. The precondition
is documented on every prediction entry point, which the reporter said
would alone have saved the day.

#43 — `cdf` was `pub(crate)`, so a consumer asking "is this competitor
below the cutoff" approximated it with a `mu + z * sigma` band and had no
way to say what confidence any `z` bought. Adds
`Gaussian::probability_below` / `probability_above`. The second is
separate on purpose: `1 - cdf` collapses to exactly zero past ~8.3
sigma, and a stopping rule is evaluated precisely there. Both route
through the survival function added in 0.4.1, so this is visibility
rather than new numerics.

#50 — `ConvergenceReport` was not `#[must_use]`, so the one signal that
a fit stopped short was trivially discarded. It now is, and that
immediately found 78 sites doing exactly that — including this crate's
own ATP example, which was capped at 10 sweeps when the history needs
30. The example now reads the report and says so.

`ITERATIONS = 30` is documented as the floor it is, with the three
measurements to hand: 400 events over 100 competitors already stops
there at ~7e-3 against a 1e-6 tolerance, the ATP example needs 30 at a
much looser one, and a consumer's 2000-node model needs 76 to 161.

BREAKING CHANGE: `InferenceError::UnknownKey` gains a `key` field, and
the prediction methods now require `K: Debug` in order to fill it.

Closes #43, #50. Refs #44 — its third ask, an opt-in `UnknownKeys::Skip`
mode, is a live API question and deliberately not answered here.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ

tests/api_shape.rs

@@ -65,7 +65,7 @@ fn add_events_draw() {
         outcome: Outcome::draw(2),
     }];
     h.add_events(events).unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 }
 
 #[test]
@@ -123,7 +123,7 @@ fn fluent_event_builder_winner_convenience() {
         .winner(0)
         .commit()
         .unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 }
 
 #[test]
@@ -141,7 +141,7 @@ fn fluent_event_builder_draw() {
         .draw()
         .commit()
         .unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 }
 
 #[test]
@@ -155,7 +155,7 @@ fn current_skill_and_learning_curve() {
         .build();
     h.record_winner(&"a", &"b", 1).unwrap();
     h.record_winner(&"a", &"b", 2).unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 
     let a = h.current_skill(&"a").unwrap();
     assert!(a.mu() > 25.0);
@@ -201,7 +201,7 @@ fn predict_quality_two_teams() {
         .p_draw(0.0)
         .build();
     h.record_winner(&"a", &"b", 1).unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 
     let q = h.predict_quality(&[&[&"a"], &[&"b"]]).unwrap();
     assert!(q > 0.0 && q <= 1.0);
@@ -217,7 +217,7 @@ fn predict_outcome_two_teams_sums_to_one() {
         .p_draw(0.0)
         .build();
     h.record_winner(&"a", &"b", 1).unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 
     let p = h.predict_outcome(&[&[&"a"], &[&"b"]]).unwrap();
     let wins = p.win_probabilities();
@@ -245,7 +245,7 @@ fn fluent_event_builder_scores() {
         .scores([12.0, 4.0])
         .commit()
         .unwrap();
-    h.converge().unwrap();
+    let _ = h.converge().unwrap();
 
     let a = h.current_skill(&"alice").unwrap();
     let b = h.current_skill(&"bob").unwrap();