Learn

Learning preferences

Make learning more comfortable. Changes apply immediately.

Enlarges page content, including diagrams and their labels. You can also use your browser’s zoom.

Choose the colours that are easiest for you to distinguish. Applies to lesson diagrams and lab plots. Labels, shapes, hatching and line patterns also distinguish meanings. Source photographs and raw image brightness stay unchanged; mask-only views are available for image overlays.

ExampleComparisonSample link

Saved in this browser on this device. Preferences are separate from progress backups.

SCIENCE · INVESTIGATE AND EXPLAIN

Keep an unknown character state distinct from observed absence

Incomplete specimens and uncertain sequence alignment can leave a character unobserved. Coding an unknown as absence invents a biological observation. Uncertainty should be retained in the matrix and propagated to the inference rather than resolved in whichever direction supports a favored tree.

Learning outcome: Compare unknown, forced0 and observed1 while keeping one1100 column. Reconstruct the score change and report every shortest split. State which additional actual observation would distinguish missing information from an observed absence.

Starting recap

Reconstruct character-based relationships, preserve topological and sampling uncertainty, and distinguish lineage, species and detection measures of diversity.

All lessons are open directly. Your saved experiments remain when you leave and return.

Try

With1?00, one1010 conflict and one1100 column, AB|CD scores1+2+1=4 and AC|BD scores1+1+2=4; AD|BC scores1+2+2=5. Observing B=1 changes the first column to1100 and gives totals4,5,6, favoring AB|CD.

Open the experiment

Predict

Does allowing a missing state to take the minimum-cost value establish that it actually had that value?

Experiment

Compare unknown, forced0 and observed1 while keeping one1100 column. Reconstruct the score change and report every shortest split. State which additional actual observation would distinguish missing information from an observed absence.

Compare ambiguous state handling with forced absence

Worked teaching model · synthetic values · Missing state? allows{0,1} during minimization; it is not coded as observed0. Original character1?00 plus selected1100 columns and one1010 conflict. Conditional minimum-change scores do not provide a probability that a missing state is1.

Reset keeps your prediction, captures and practice history.

Unknown does not mean absent: preserve ambiguity instead of manufacturing an observed state.

Allowing both states may lower a score or leave several equally short trees. This is optimization over uncertainty, not evidence that the unobserved state took whichever value favors a preferred tree. Report ties and obtain independent data.

Taxa A B C D; columns: 1?00 ; 1010 ; 1100

Calculated quantities under stated assumptions
QuantityValueUnit
AB|CD minimum total changes4changes
AC|BD minimum total changes4changes
AD|BC minimum total changes5changes

Retain prior predictions, settings and quantities in the notebook. These are original supplied/calculated comparisons, not physical specimen observations or laboratory participation.

Unknown does not mean absent: preserve ambiguity instead of manufacturing an observed state.

Allowing both states may lower a score or leave several equally short trees. This is optimization over uncertainty, not evidence that the unobserved state took whichever value favors a preferred tree. Report ties and obtain independent data.

Captures preserve recipes and replay their calculated results. They are synthetic model evidence, not physical measurements.

Understand

1. Orient: Keep an unknown character state distinct from observed absence

Incomplete specimens and uncertain sequence alignment can leave a character unobserved. Coding an unknown as absence invents a biological observation. Uncertainty should be retained in the matrix and propagated to the inference rather than resolved in whichever direction supports a favored tree.

2. Explain the evidence and inference model

The supplied character is1?00 in order A,B,C,D. The unknown B can be0 or1 during minimum-change optimization. A second character1010 and optional1100 columns provide conflicting or supporting evidence. The model compares the true unknown code with forced0 and observed1, showing that those inputs express different knowledge.

Fitch optimization with an unknown starts that tip with set1. A resulting low score means at least one allowed assignment realizes that cost. It is not a posterior probability, a measured state or proof of a particular evolutionary history. Several trees may tie because evidence is insufficient or conflicting.

A strict consensus retains only clades shared by all retained trees; a polytomy can express unresolved branching rather than a claim that all divergences occurred simultaneously. Collecting a genuinely informative missing character can resolve uncertainty, but repeated uninformative observations cannot guarantee it.

3. Reconstruct a worked example

With1?00, one1010 conflict and one1100 column, AB|CD scores1+2+1=4 and AC|BD scores1+1+2=4; AD|BC scores1+2+2=5. Observing B=1 changes the first column to1100 and gives totals4,5,6, favoring AB|CD.

4. Predict and investigate

Predict first: Does allowing a missing state to take the minimum-cost value establish that it actually had that value?

Compare unknown, forced0 and observed1 while keeping one1100 column. Reconstruct the score change and report every shortest split. State which additional actual observation would distinguish missing information from an observed absence.

Retain the complete matrix, topology, branch lengths or detection assumptions with the first prediction and all conflicting results. These original hypothetical data are not actual specimen observations or measured evolutionary histories.

5. Transfer and defend a changed case

Apply the mechanism to a changed case

Use unknown1?00 with1010 but no additional1100 column. Which split is shortest?

Retain your calculation, units and assumptions in the lesson notebook before opening this public practice response.

Compare a worked response

AB|CD costs1+2=3, AC|BD costs1+1=2 and AD|BC costs1+2=3. AC|BD is shortest under this limited packet, but the unknown state has not been measured and broader evidence can change the result.

6. Reflect and preserve uncertainty

Explain which conclusion is conditional on the model, identify an alternative and propose independent discriminating evidence. Use the General Biology II workspace to preserve a first argument, human critique and revision. Public practice and submitted prose do not establish independent phylogenetic, sampling or practical competence.

Supporting scientific reference, checked 30 October 2026. Original prose, numerical packets and diagrams; no source artwork or exercise is reproduced and no organism handling is requested.

Apply

An unknown binary state should initially be represented as:

Public practice checks a specific claim. It does not establish independent mastery.

Use unknown1?00 with1010 but no additional1100 column. Which split is shortest?

Continue

Completion requires a saved prediction, at least one capture and a reflection. Presence checks do not grade your reasoning.

Skipping retains experiments and awards no mastery.

Save a science backup or import one

Science work is stored locally in this browser, separately from mathematics and AI. It does not sync automatically. Use this science-only backup; this checkout's older all-subject export does not include science.

A legacy file is available only when the complete snapshot fits the 36 MB limit. For larger snapshots, prepare a complete file set below; never discard notebooks to make a smaller file.

Matching imported notebook drafts replace their current drafts. Omitted lessons and known first attempts remain. Export your current work first if you want to retain both drafts.

Complete file sets and course backups

A file set can hold more work than one science file. Keep its manifest and every part together. Each file is limited to 36 MB. This format needs this version of LearnAI or a compatible newer version; older versions can use only the legacy single-file backup above.

The set includes every saved science course and global continuation at this moment. Later edits need a new export. A conflicting tab exports its own visible work, which may differ from saved work in another tab.

0 file(s) selected. File order and filenames do not determine identity. Missing, changed or conflicting parts block the entire restore; no good part is applied by itself.

Science storage: loading. No account or remote computation is required.