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GrimTide GrimTide
GrimTide GrimTide
I’ve been digging into the case of the vanished 18th‑century brig “The Sphinx” – a ship rumored to have had an experimental hull that could have changed naval tactics. I’d love to hear how you’d approach optimizing a search for that mystery ship with your spreadsheet skills.
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Okay, let’s treat this like a mission objective. First, build a master sheet called “Sphinx Hunt” with tabs: Sources, Candidates, Weather, Resources, KPI Tracker. In Sources, list every archive, ship log, newspaper, and even local lore sites, each with a priority score and reliability weight. In Candidates, log every possible wreck location – depth, coordinates, estimated time period, and a probability score that updates automatically. Use a simple formula: Probability = (Historical Likelihood * Reliability Weight * Condition Score). Weather tab pulls API data for sea currents and visibility for each candidate site. Resource tab tracks budget, dive teams, and equipment, calculating cost per probability unit. KPI Tracker monitors “Search Efficiency” = (Total Probability Covered ÷ Total Hours Spent). If the KPI drops below 0.6, pivot to the next highest probability zone. Keep a “Learning Log” to adjust weights after each dive. That’s a 1‑page system that turns a mystery into a data‑driven playbook.
GrimTide GrimTide
Sounds like a solid framework, but don’t forget that the “Reliability Weight” on some of those local lore sites will probably have to be capped. I’d add a quick sanity check for any source that has a high priority but a low corroboration rate. And when you’re pulling the weather API, keep a manual log in case the feed hiccups—those currents can shift in a flash and throw a diver’s plan off. The KPI pivot rule is clever, just make sure you still have a buffer for unplanned detours; sometimes the best finds come from a last‑minute change of mind. Good plan.
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That’s a great add‑on—capping the reliability on folklore sites keeps the model honest. I’ll add a conditional column that flags any source with a priority above 8 but a corroboration below 3, and that will auto‑dim its probability weight. For the weather API, I’ll create a “Backup Log” sheet that records a timestamped snapshot of the feed; if the API goes down, the diver can still rely on the last known data. And I’ll tweak the KPI pivot rule to include a “Contingency Buffer” of 10% of the total probability so that a spontaneous detour still earns a full credit. That way, the system rewards flexibility without losing its discipline.
GrimTide GrimTide
Nice touch with the conditional dimming—keeps the model from chasing phantom leads. I’ll just add a note in the “Learning Log” that any source flagged like that still gets a quick review after the dive; sometimes a single corroborating note can swing the weight enough to warrant a second look. Good call on the contingency buffer; a 10 percent safety net feels right. Keeps the team from feeling punished for following a hunch that turns out to be a dead end. Sounds like you’re on the right track.