Grading Throughput
Plan batch grading duration with repeat passes, productive fraction and per-batch setup; reports unique fish throughput rather than counting repeated passes as new fish.
Description
Grading duration includes each repeated pass through the machine and per-batch setup.
Grading duration includes each repeated pass through the machine and per-batch setup. Unique fish throughput is lower than pass throughput when fish are processed more than once.1
Plan batch grading duration with repeat passes, productive fraction and per-batch setup; reports unique fish throughput rather than counting repeated passes as new fish.
When to use Grading Throughput
Use this tool to plan batch grading duration with repeat passes, productive fraction and per-batch setup; reports unique fish throughput rather than counting repeated passes as new fish.
Prepare the cohort, water, feed or equipment measurements for one consistent operating period. Confirm nitrogen reporting bases, dry/wet masses, flow units and supplied design assumptions.
How to use Grading Throughput and understand its results
- Prepare the cohort, water, feed or equipment measurements for one consistent operating period. Confirm nitrogen reporting bases, dry/wet masses, flow units and supplied design assumptions.
- Enter batchFishCounts, graderNominalFishPerHour, productiveFraction, requiredPasses, setupHoursPerBatch using the field table and calculation rules, then run the tool.
- Read batchHours, totalGradingHours, overallEffectiveFishPerHour. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| batchFishCounts | Batch fish counts | list of number | Required |
| graderNominalFishPerHour | Grader nominal fish per hour | number | Required |
| productiveFraction | Productive fraction | number | Required |
| requiredPasses | Required passes | integer | Required |
| setupHoursPerBatch | Setup hours per batch | number | Required |
Enter a nonempty list of nonnegative integer batch counts, positive nominal fish/hour, productive fraction greater than zero through one, positive integer passes and nonnegative setup hours. Calculate each batch's hours and sum them; total hours must be positive. Return unique-fish effective throughput.
batchHours=batchFish×passes/(nominalRate×productiveFraction)+setup; effectiveRate=totalUniqueFish/totalHours Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| batchHours | Batch hours | list of number |
| totalGradingHours | Total grading hours | number |
| overallEffectiveFishPerHour | Overall effective fish per hour | number |
{
"input": {
"batchFishCounts": [
1000,
2000
],
"graderNominalFishPerHour": 2000,
"productiveFraction": 0.5,
"requiredPasses": 2,
"setupHoursPerBatch": 0.25
},
"output": {
"batchHours": [
2.25,
4.25
],
"totalGradingHours": 6.5,
"overallEffectiveFishPerHour": 461.53846153846155
}
}Limitations and assumptions
Rate and productive fraction are constant across batches. Handling welfare, fish-size changes and scheduling conflicts are not modeled; zero-count batches still receive the supplied setup time.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Grading Size Variation: Calculate weighted population size CV and grading counts from weight/frequency observations; bins are left-closed at boundaries and no cannibalism threshold is inferred. For a complementary task, use Fry Stocking: Invert serial expected survival to a rounded initial fry count; an expectation-based plan, not a probability guarantee or density recommendation.
References
-
Grading Throughput — tool.io
Similar or alternative tools
- Grading Size Variation
Calculate weighted population size CV and grading counts from weight/frequency observations; bins are left-closed at boundaries and no cannibalism threshold is inferred.
- Fry Stocking
Invert serial expected survival to a rounded initial fry count; an expectation-based plan, not a probability guarantee or density recommendation.