basal — primary-source ecological data for loan-review committees

How it runs

SD card in. Audit-traceable
exposure record out.

Every transformation is a cited method. Every intermediate value persists, so an external auditor can inspect any step — detection rate, bias correction, density estimate, tier call — independently.

pipeline inputs

detections · camera-days · placement_context · boundary

methodology outputs

rate · adj_rate · density · CI · tier · score

supplementary

modeled $ · crop modifier · ESA flags

Worked example · Farm Credit of Central Texas

Riverbend Farm, Brazos Co. — 650 acres of corn.

Severe TX-BRA-2026-00012

83.7 / 100 Feral hog exposure score

13.47 animals/km² 95% CI 3.49 – 36.56

1.526 raw rate/cam-day pre-bias correction

1.042 IPW-adjusted rate REM consumed

Trajectory

Fall 2025 Elevated 5.66 /km² ⟶ Spring 2026 Severe 13.47 /km²

+138% density since the prior survey. The trajectory a $40K point-in-time field survey would have missed.

Open full report PDF JSON API

CAM-RB-RAND-01 · IR

random anchor

REC

event 94 of 354

2026-02-28 · 23:14:51

Modeled projection

$22,955/year

not a pipeline output

Scaled from Anderson 2016 per-hog damage × 650 ac × corn modifier 1.60. Third-party loss data; a committee with an internal damage model should consume the pipeline outputs directly.

Methodology

Published math.
Inspectable every step.

An external auditor can recover every value we report. The random-placement reference camera on each parcel anchors the IPW correction; the bootstrap propagates the weighting uncertainty into the CI; the tier cutoff is a published binning.

01 / index

Detection frequency — independent events per camera-day. Pre-REM relative abundance. Zero movement assumption. Directly comparable across identical deployments.

02 / debias

IPW correction — per-camera rate deflated by a literature-prior inflation factor (Kolowski & Forrester 2017) keyed to placement_context. Hájek IPW reported alongside as a diagnostic. Random-placement cameras anchor the correction against an unbiased reference.

03 / density

Random Encounter Model — D = (y/t) · π / (v · r · (2 + θ)). Per-species v: hog 6.0 km/day (Kay 2017), deer 1.5 (Webb 2010), coyote 10.0 (Andelt 1985). 1000-iteration bootstrap over cameras with truncated v-perturbation.

04 / tier

Mayer-Brisbin 2009 cutoffs. <2/km² Low · 2-5 Moderate · 5-10 Elevated · ≥10 Severe. Feral hog only at v1; other species surfaced as Informational with density where v is published.

05 / flag

Recommendation — insufficient_data below 100 cam-days or 20 events; recommend_supplementary_survey when CI upper/lower > 1.5; else sufficient_for_decision.

Pricing

25× cheaper than a field survey.
Continuous instead of point-in-time.

A loan-review committee's alternative is a $40,000 independent biologist survey that's stale in six months. We refresh on any new upload.

Per verification

$1,500/parcel

One report per survey window. Use it for a single collateral review.

Full Nature Exposure Report (HTML + JSON)
Methodology appendix + 12 citations
JSON API for internal portfolio systems
Camera-day-level audit trail

Portfolio unlimited

$5,000/month

Unlimited parcels, continuous refresh on every upload.

Every parcel in your portfolio
Survey-trend delta across all periods
Cross-portfolio tier distribution
Ag-lender dashboard + exports
Regulatory-affairs methodology briefing

SD card in. Audit-traceable exposure record out.

Riverbend Farm, Brazos Co. — 650 acres of corn.

Published math. Inspectable every step.

25× cheaper than a field survey. Continuous instead of point-in-time.

SD card in. Audit-traceable
exposure record out.

Published math.
Inspectable every step.

25× cheaper than a field survey.
Continuous instead of point-in-time.