GunSpec

Alle 39 Einzel-Tools

Load carriage cost

  • gunspec_load_carriage
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Returns the metabolic energy a foot march costs carrying each of one to five firearms, with its magazines and attachments, on top of the other kit carried, by the US Army LCDA equation (Looney et al. 2022) or the Pandolf equation (1977). Use this tool when the question is how much harder one firearm is to carry than another over a distance. Masses come from the catalog; a firearm missing its empty or loaded weight comes back with an error instead of a cost. The answer is energy cost only: it does not predict fatigue, marksmanship or time to exhaustion.

Anwendungsbeispiel

  1. 1

    Eine Person fragt

    • How much more energy does a 20 km march cost carrying an FN SCAR-L than an HK416, with seven magazines and 25 kg of kit?
  2. 2

    Der Assistent ruft das Tool auf

    tools/call
    json
    {  "name": "gunspec_load_carriage",  "arguments": {    "ids": [      "hk416",      "fn-scar-l"    ],    "magazines": 7,    "kit_kg": 25,    "distance_km": 20  }}
  3. 3

    Der Server antwortet

    Ergebnis
    json
    {  "model": {    "id": "lcda",    "name": "LCDA backpacking equation with the LCDA graded-walking term",    "equation": "M (W/kg body mass) = Mrest + (0.19 + η(1.78·S^0.58 + 0.27·S^4 + Mgrade)) · (1 + 1.96·L^1.36); Mgrade = 34·S·G·(1 − 1.05^(1 − 1.1^(100·G + 32))); S in m/s, G as rise/run, L = load ÷ body mass; Mrest from Cunningham (1991): (500 + 22·lean mas…",    "citations": [      {        "authors": "Looney DP, Lavoie EM, Vangala SV, Holden LD, Figueiredo PS, Friedl KE, et al.",        "year": 2022,        "title": "Modeling the Metabolic Costs of Heavy Military Backpacking",        "journal": "Medicine & Science in Sports & Exercise 54(4):646-654",        "doi": "10.1249/MSS.0000000000002833"      },      {        "authors": "Looney DP, Santee WR, Hansen EO, Bonventre PJ, Chalmers CR, Potter AW",        "year": 2019,        "title": "Estimating Energy Expenditure during Level, Uphill, and Downhill Walking",        "journal": "Medicine & Science in Sports & Exercise 51(9):1954-1960",        "doi": "10.1249/MSS.0000000000002002"      },      {        "authors": "Soule RG, Goldman RF",        "year": 1972,        "title": "Terrain coefficients for energy cost prediction",        "journal": "Journal of Applied Physiology 32(5):706-708",        "doi": "10.1152/jappl.1972.32.5.706"      }    ],    "validated": {      "speedMps": [        0.45,        1.97      ],      "loadFraction": [        0,        0.66      ]    }  },  "march": {    "bodyMassKg": 80,    "bodyFatPct": 15,    "kitKg": 25,    "magazines": 7,    "speedKmh": 4.8,    "speedMps": 1.333,    "gradePct": 0,    "terrain": "paved",    "terrainFactor": 1,    "distanceKm": 20,    "durationS": 15000,    "defaulted": [      "body_mass_kg",      "body_fat_pct",      "speed_kmh"    ]  },  "attachments": [],  "results": [    {      "firearm": {        "id": "hk416",        "name": "HK416",        "magazineType": "detachable_box",        "magazineCapacity": 30      },      "load": {        "firearmEmptyG": 3020,        "loadedMagazineG": 400,        "magazines": 7,        "magazinesG": 2800,        "attachmentsG": 0,        "carriedG": 5820,        "totalLoadKg": 30.82,        "loadFraction": 0.385,        "sources": {          "firearmEmptyG": "catalogue",          "loadedMagazineG": "derived"        }      },      "cost": {        "watts": 483.2,        "wattsPerKg": 6.04,        "energyKj": 7248.2,        "energyKcal": 1732.4      },      "delta": null,      "warnings": [],      "error": null    },    {      "firearm": {        "id": "fn-scar-l",        "name": "FN SCAR-L",        "magazineType": "detachable_box",        "magazineCapacity": 30      },      "load": {        "firearmEmptyG": 3500,        "loadedMagazineG": 490,        "magazines": 7,        "magazinesG": 3430,        "attachmentsG": 0,        "carriedG": 6930,        "totalLoadKg": 31.93,        "loadFraction": 0.399,        "sources": {          "firearmEmptyG": "catalogue",          "loadedMagazineG": "derived"        }      },      "cost": {        "watts": 489.9,        "wattsPerKg": 6.12,        "energyKj": 7347.9,        "energyKcal": 1756.2      },      "delta": {        "carriedG": 1110,        "watts": 6.6,        "energyKj": 99.7,        "energyKcal": 23.8      },      "warnings": [],      "error": null    }  ]}

    Am 22. Okt. 2026 von der Live-API erfasst. Listen und lange Texte sind für die Anzeige gekürzt.

Zugehörige Referenzen

Argumente

  • string[]erforderlich

    One to five firearm slugs.

    Gesendet als
    ?ids=
  • numberstandard80

    Body mass of the person marching, in kg.

    Bereich
    40 – 160
    Gesendet als
    ?body_mass_kg=
  • numberstandard15

    Body fat as a percentage of body mass.

    Bereich
    3 – 50
    Gesendet als
    ?body_fat_pct=
  • numberstandard0

    Everything else carried, in kg: armour, pack, water.

    Bereich
    0 – 120
    Gesendet als
    ?kit_kg=
  • integerstandard1

    Full magazines carried in total, the one in the firearm included. 0 carries it unloaded.

    Bereich
    0 – 20
    Gesendet als
    ?magazines=
  • string[]

    Up to ten attachment slugs, added to every firearm by their recorded weight.

    Gesendet als
    ?attachments=
  • numberstandard4.8

    Marching speed in km/h.

    Bereich
    1 – 10
    Gesendet als
    ?speed_kmh=
  • numberstandard0

    Slope as a percentage, rise over run; negative is downhill.

    Bereich
    -30 – 30
    Gesendet als
    ?grade_pct=
  • stringstandardpaved

    The surface marched over.

    Akzeptiert
    Gesendet als
    ?terrain=
  • numberstandard20

    March distance in km.

    Bereich
    0.1 – 200
    Gesendet als
    ?distance_km=
  • stringstandardlcda

    The equation: lcda, or pandolf for comparison with older studies (level and uphill only).

    Akzeptiert
    Gesendet als
    ?model=

Jedes Argument wird als der angegebene Parameter an GET /v1/firearms/load-carriage gesendet. Dieselbe Anfrage lässt sich also mit deinem Schlüssel direkt an die API stellen, wenn du bestimmen möchtest, was zurückkommt.

json