Bibliographic record
Abstract
Direct field comparisons revealed that in any time period, a bat detecting system using zero-crossing period meter analysis (the Anabat II Bat Detector with Anabat ZCAIM and Anabat 6 software) detected significantly fewer bat echolocation calls than a time-expansion bat detecting system (Pettersson D980 detector with BatSoundPro software). Furthermore, the features of 81 echolocation calls (highest frequency, in kHz; lowest frequency, in kHz; duration, in ms) recorded and analyzed on both systems differed significantly. Regression analyses indicated no consistent, frequently unpredictable differences between Anabat and Pettersson values for the lowest frequencies in echolocation calls, but a significant correlation for their highest frequencies and durations. In a variety of field settings in Israel and in southern Ontario, Canada involving both foraging bats and bats emerging from a cave roost, the Pettersson system recorded echolocation calls not detected by the Anabat system. When many Myotis bats were emerging from a cave roost in Israel, the Anabat system did not detect the calls of a Rhinolophus species or those of another vespertilionid which were detected by the Pettersson system. The differences in performance between the two kinds of systems reflect differences in sensitivity and operation between zero-crossing period meters and time-expansion systems. Data on bat activity or echolocation calls detected and analyzed by a zero-crossing period meter system like Anabat are not as consistent or as reliable as those obtained by a time-expansion system like the Pettersson. Differences in performance of bat detectors coincide with considerable difference in costs, from about US$ 650 for an Anabat system, to over US$ 2,000 for a Pettersson system, which involves digital time-expansion. A time-expansion system involving a high speed tape recorder will cost over US$ 30,000. When it comes to bat detectors and analysis systems, the quality of data that will be obtained is a direct reflection of cost -- buyers get what they pay for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".