Building the First Provenance-Tagged Crossover Model

Before selecting an architecture, NASA can build a decision-grade model that compares growing food with resupply. Its inputs need sources, its conversions need rules, and its crossovers need defined mission scenarios. The first result may be a range, which is appropriate when the evidence has not earned a point estimate. The important result is a model that reveals which measurements could change a decision and retains the assumptions later teams will need to inspect.

Gastronaut can join ORCA operating data with food-service outcomes, reliability, crew time, and logistics assumptions. Cultivation, resupply, and a mixed architecture must each remain possible outcomes for a given mission phase. That discipline keeps the record useful to NASA and reviewable by investors as it moves from one decision to the next.

What earlier models establish

The bounded space-food economics synthesis assessed 39 abstracts drawn from 2,704 unique records. It assigned 21 an evidence tier and left three pending. Full-text retrieval did not run, and the search and sampling design left a substantial set of logistics records outside the reading pass. The reviewed set identifies methods and examples. It is not a complete economic review, so the crossover model must retain that boundary.

Equivalent system mass is the common method. Drysdale and colleagues evaluated life-support approaches by converting significant costs to mass equivalents. Their abstract reports that the preferred architecture depends on mission duration, environment, infrastructure, crew size, and available technology. Physicochemical regeneration was most cost-effective for the studied missions. Bioregeneration entered in stages, from salad crops to staple crops and then broader closure at long duration (Drysdale et al. 2003).

A related lunar model placed closure on a timeline. For selected scenarios, Drysdale described water closure at about one month, oxygen and carbon dioxide closure at two months, and food closure three months after the first staple harvest. The paper also compared supply-based, physicochemical, remote, and crew-ramp startup strategies over a ten-year modelling horizon (Drysdale 1994). These are architecture-model outputs rather than operating records.

Other studies identify variables that must remain visible. Stromgren and colleagues found that more regenerative ECLSS capability can reduce recurring logistics while raising initial delivery mass and adding maintenance and spares (Stromgren et al. 2022). Jones reported a possible spares mass near 100 percent of the original system mass for ultra-reliable life support under stated reliability conditions (Jones 2009). Olabi and colleagues found that food-preparation tasks do not scale uniformly with crew size, which calls for task-specific labor models (Olabi et al. 1999).

The 21 tiered records contained no observed money price for crew food. One of 23 rows with the field stated a cost basis year. The first crossover model can therefore begin in physical and operational units, where the evidence is firmer. Monetary scenarios can follow when sourced estimates include scope, payer, basis year, and uncertainty.

Mission duration, startup strategy, reliability, and crew time can move a crossover with high confidence. Confidence is moderate that provenance tags can identify the gaps most likely to change a decision. Confidence in a current ORCA crossover point is low because the platform has no flight data and the comparator remains unbounded. The appropriate response is to order the next measurements carefully rather than force a premature result.

Establish the comparison boundary

The model begins with the mission: destination, crew size, surface or transit duration, campaign length, delivery cadence, available habitat volume, power and cooling factors, water-recovery architecture, communications, maintenance concept, and contingency policy. Agreement here protects the comparisons that follow from shifting boundaries.

The food-service definition follows. The model can compare safe edible mass, nutrient delivery, menu contribution, or a contingency capability. Cultivation and resupply must share the same numerator and denominator. If crop production receives credit for fresh-food acceptability or research value, those outcomes need a measurement and visible weighting rather than an assumed premium.

The system boundary should include development and test equipment where relevant, initial delivery, installation, startup consumables, crop inputs, packaging, storage, water, nutrients, gases, power, heat rejection, crew time, ground support, spares, maintenance, failures, lost crops, recovery, waste, and end-of-life disposition. Sunk development cost remains separate from mission-specific recurring cost.

Give every input a provenance tag

Each model value should carry:

• source and stable identifier;
• evidence type: measured, derived, modelled, quoted, or assumed;
• system configuration and mission scenario;
• unit, boundary, and basis year where money is used;
• central value, range, distribution, or confidence statement;
• version, owner, and date;
• sensitivity rank and replacement plan.

This structure prevents a historical model factor from becoming an ORCA measurement through repetition. It also lets a test result replace an early estimate without rebuilding the model’s logic. The record can mature while retaining its history.

Build through four decisions

The first decision asks whether the model reproduces published examples under their stated assumptions. A pass validates implementation, not the assumptions themselves. This establishes a sound beginning without granting the model more authority than the test provides.

The second decision asks which inputs existing Gastronaut ground data can populate. ORCA is at approximately TRL 3 to 4, with no flight or lunar cycles. Its 1,042-cycle internal record may inform yield variability, task categories, interventions, failure modes, and recovery. It does not establish mission reliability, food safety, nutritional contribution, or lunar resource use.

The third decision selects prospective ground tests by value of information. If crew-time uncertainty drives the crossover, the test measures tasks. If lost-crop probability dominates, it examines faults and recovery. If pressurized volume dominates, it compares configuration assumptions. In each case, the model directs the experiment toward the uncertainty that matters to the decision, respecting the resources entrusted to the test.

The fourth decision asks whether the record warrants an analog or flight feasibility study. NASA can examine the sensitivity range, unresolved assumptions, and evidence expected from the next stage. Investors can use the same technical record for diligence without substituting it for a NASA acquisition decision. One record supports both forms of judgment while preserving the boundary between them.

A responsible deliverable

Gastronaut proposes an auditable model, input ledger, scenario library, sensitivity analysis, and measurement roadmap. The model would report the mission conditions under which cultivation, resupply, or a mixed architecture becomes preferred, together with the uncertainty around each boundary. NASA could use it for a research decision; investors could examine how each test changes the technical proposition. Those decisions remain separate even when they draw from one record, and the model should continue to serve both by keeping its evidence visible.

The first crossover may move as evidence improves. A model serves the mission when it changes with the facts, preserves why it changed, and identifies the next fact worth learning.

References

Drysdale, Alan E. “Lunar Bioregenerative Life Support Modeling.” SAE Technical Paper, 1994. https://doi.org/10.4271/941456.

Drysdale, Alan E., et al. “Life Support Approaches for Mars Missions.” Advances in Space Research, 2003. https://doi.org/10.1016/S0273-1177(02)00658-0.

Gastronaut. Space Food Economics: Evidence Synthesis. Report GAS-B4-ECO-20260822, evidence version 22 Aug. 2026. Research synthesis.

Gastronaut. ORCA Public-Safe System and Evidence Baseline. Evidence version 23 Aug. 2026. Company technical record.

Jones, Harry. “Developing Ultra Reliable Life Support for the Moon and Mars.” International Conference on Environmental Systems, 2009.

Olabi, Ammar, et al. “Work Measurement Videotaping Technique as a Means for Estimating Food Preparation Labor Time of a Bioregenerative Diet.” SAE Technical Paper, 1999. https://doi.org/10.4271/1999-01-2075.

Stromgren, Chel, et al. “Regenerative ECLSS and Logistics Analysis for Sustained Lunar Surface Missions.” IEEE Aerospace Conference, 2022. https://doi.org/10.1109/AERO53065.2022.9843674.