This vignette walks both modules end-to-end on the canonical Ferralsol fixture, with all external dependencies (raster files, OSSL parquet libraries, ellmer chats) replaced by inline synthetic objects so the example is fully reproducible.

1. Start from a partially-described pedon

Take the canonical Latossolo and intentionally erase the CEC and base-saturation values from the lower horizons. We will fill them back in with simulated OSSL predictions in §3.

pr_full   <- make_ferralsol_canonical()
pr_partial <- pr_full$clone(deep = TRUE)
pr_partial$horizons[3:5, c("cec_cmol", "bs_pct") := NA]

pr_partial$horizons[, .(top_cm, bottom_cm, designation,
                        clay_pct, cec_cmol, bs_pct, oc_pct)]
#>    top_cm bottom_cm designation clay_pct cec_cmol bs_pct oc_pct
#>     <num>     <num>      <char>    <num>    <num>  <num>  <num>
#> 1:      0        15           A       50      8.0     24    2.0
#> 2:     15        35          AB       52      6.5     17    1.2
#> 3:     35        65          BA       55       NA     NA    0.6
#> 4:     65       130         Bw1       60       NA     NA    0.3
#> 5:    130       200         Bw2       60       NA     NA    0.2

The classification on this incomplete pedon already differs:

res_partial <- classify_wrb2022(pr_partial, on_missing = "silent")
res_partial$rsg_or_order
#> [1] "Nitisols"
res_partial$evidence_grade
#> [1] "A"

The evidence grade reflects the missing data – the per-RSG trace records which RSGs returned NA because of attributes we erased.

2. Module 3 – spatial prior consistency check

The prior is a probability vector over RSGs from any source – a regional SoilGrids extract, a national Embrapa map, an interpolated kriging surface, etc. For this vignette we build it inline so the example runs without network access:

# Synthetic prior consistent with the gneiss-Mata-Atlantica context:
# Ferralsols dominate, with a tail of Acrisols and Cambisols.
prior <- data.table::data.table(
  rsg_code    = c("FR", "AC", "CM", "AL"),
  probability = c(0.62, 0.20, 0.12, 0.06)
)
prior
#>    rsg_code probability
#>      <char>       <num>
#> 1:       FR        0.62
#> 2:       AC        0.20
#> 3:       CM        0.12
#> 4:       AL        0.06

prior_consistency_check() confirms the deterministic call (FR) is supported by the prior:

chk <- prior_consistency_check(rsg_code = "FR", prior = prior, threshold = 0.05)
chk
#> $consistent
#> [1] TRUE
#> 
#> $p
#> [1] 0.62
#> 
#> $threshold
#> [1] 0.05
#> 
#> $status
#> [1] "consistent"
#> 
#> $note
#> [1] "Assigned RSG 'FR' has prior probability 0.620 at this location (>= threshold 0.050)."
#> 
#> $top_prior
#>    rsg_code probability
#>      <char>       <num>
#> 1:       FR        0.62
#> 2:       AC        0.20
#> 3:       CM        0.12

Now suppose the deterministic key had instead landed on Cambisols. The same prior would flag the disagreement (Cambisols at probability 0.12 vs the dominant Ferralsols at 0.62 – the inconsistency margin):

prior_consistency_check(rsg_code = "AL", prior = prior, threshold = 0.05)
#> $consistent
#> [1] TRUE
#> 
#> $p
#> [1] 0.06
#> 
#> $threshold
#> [1] 0.05
#> 
#> $status
#> [1] "consistent"
#> 
#> $note
#> [1] "Assigned RSG 'AL' has prior probability 0.060 at this location (>= threshold 0.050)."
#> 
#> $top_prior
#>    rsg_code probability
#>      <char>       <num>
#> 1:       FR        0.62
#> 2:       AC        0.20
#> 3:       CM        0.12

The deterministic key is never overwritten by the prior. The check only flags cases where a manual review is warranted; the user remains in charge of the final assignment. Real production runs would source the prior from spatial_prior_soilgrids() (a live SoilGrids-WCS request) or spatial_prior_embrapa():

prior <- spatial_prior_soilgrids(pr_partial, buffer_m = 250)

3. Module 4 – OSSL gap-filling

The OSSL workflow is:

Pre-process the raw spectra (SNV / Savitzky-Golay 1st derivative).
Send each horizon’s spectrum through one of three predictors:
- predict_ossl_mbl() – memory-based learning (recommended);
- predict_ossl_plsr_local() – partial-least-squares with a local subset;
- predict_ossl_pretrained() – a pre-trained Cubist or RF model.
Convert each property’s prediction-interval width to an A–D confidence grade via pi_to_confidence().
fill_from_spectra() writes each predicted value into the horizons table AND adds a provenance entry with source = "predicted_spectra".

A production call would look like this (skipped in this vignette because OSSL is a multi-GB dataset that would have to be downloaded):

fill_from_spectra(
  pr_partial,
  library     = "ossl",
  region      = "south_america",
  properties  = c("clay_pct", "cec_cmol", "bs_pct", "oc_pct"),
  method      = "mbl",
  preprocess  = "snv+sg1",
  k_neighbors = 100L,
  ossl_library = "/path/to/ossl-soilsite-vnir.parquet"
)

For the vignette, simulate the predicted values directly through pedon$add_measurement():

preds <- list(
  list(idx = 3, attribute = "cec_cmol", value = 5.5, confidence = 0.78),
  list(idx = 3, attribute = "bs_pct",   value = 14,  confidence = 0.72),
  list(idx = 4, attribute = "cec_cmol", value = 4.9, confidence = 0.79),
  list(idx = 4, attribute = "bs_pct",   value = 13,  confidence = 0.74),
  list(idx = 5, attribute = "cec_cmol", value = 4.7, confidence = 0.70),
  list(idx = 5, attribute = "bs_pct",   value = 13,  confidence = 0.71)
)

pr_filled <- pr_partial$clone(deep = TRUE)
for (p in preds) {
  pr_filled$add_measurement(
    horizon_idx = p$idx,
    attribute   = p$attribute,
    value       = p$value,
    source      = "predicted_spectra",
    confidence  = p$confidence,
    overwrite   = TRUE
  )
}

pr_filled$horizons[, .(top_cm, bottom_cm, cec_cmol, bs_pct)]
#>    top_cm bottom_cm cec_cmol bs_pct
#>     <num>     <num>    <num>  <num>
#> 1:      0        15      8.0     24
#> 2:     15        35      6.5     17
#> 3:     35        65      5.5     14
#> 4:     65       130      4.9     13
#> 5:    130       200      4.7     13

The predicted values are now in the horizons table, and the provenance log records each as predicted_spectra:

prov <- pr_filled$provenance
prov[source == "predicted_spectra", .(horizon_idx, attribute, source, confidence)]
#>    horizon_idx attribute            source confidence
#>          <int>    <char>            <char>      <num>
#> 1:           3  cec_cmol predicted_spectra       0.78
#> 2:           3    bs_pct predicted_spectra       0.72
#> 3:           4  cec_cmol predicted_spectra       0.79
#> 4:           4    bs_pct predicted_spectra       0.74
#> 5:           5  cec_cmol predicted_spectra       0.70
#> 6:           5    bs_pct predicted_spectra       0.71

4. Re-classify with the gap-filled pedon

After OSSL fills the missing CEC/BS, the deterministic key has a complete dataset and the classification’s evidence grade reflects the predicted source.

res_filled <- classify_wrb2022(pr_filled, on_missing = "silent")
res_filled$rsg_or_order
#> [1] "Ferralsols"
res_filled$evidence_grade
#> [1] "B"
res_filled$name
#> [1] "Geric Ferric Rhodic Chromic Ferralsol (Clayic, Humic, Dystric, Ochric, Rubic)"

The evidence grade ladder for the same profile across the three workflows:

Workflow	Evidence grade
Lab-only (full canonical fixture)	A
Spectra-filled (OSSL `predicted_spectra`)	B
VLM-extracted only	C
User-assumed	D

5. Combining priors and posteriors

combine_priors() merges multiple sources (SoilGrids + Embrapa + a custom map) with weights, returning a single normalised prior that you can feed into prior_consistency_check():

combined <- combine_priors(
  priors = list(
    soilgrids = data.table::data.table(rsg_code = c("FR", "AC", "CM"),
                                          probability = c(0.62, 0.20, 0.18)),
    embrapa   = data.table::data.table(rsg_code = c("FR", "AC", "NT"),
                                          probability = c(0.55, 0.30, 0.15))
  ),
  weights = c(soilgrids = 0.6, embrapa = 0.4)
)
combined
#>    rsg_code  probability
#>      <char>        <num>
#> 1:       FR 0.7139748011
#> 2:       AC 0.2841641547
#> 3:       CM 0.0017189621
#> 4:       NT 0.0001420821

posterior_classify() would then take a ClassificationResult and a prior, returning a posterior probability over RSGs (the deterministic key contributes a sharply peaked likelihood). Used for ranking ambiguous fixtures or for active-learning loops.

Spatial prior + OSSL spectra pipeline (Modules 3 & 4)

1. Start from a partially-described pedon

2. Module 3 – spatial prior consistency check

3. Module 4 – OSSL gap-filling

4. Re-classify with the gap-filled pedon

5. Combining priors and posteriors

Summary